All charts

## Load libraries
library(covid19)
library(ggplot2)
library(lubridate)
library(dplyr)
library(ggplot2)
library(sp)
library(raster)
library(viridis)
library(ggthemes)
library(sf)
library(rnaturalearth)
library(rnaturalearthdata)
library(RColorBrewer)
library(readr)
library(zoo)
library(tidyr)
options(scipen = '999')

Daily cases Spain

pd <- df_country %>%
  filter(country == 'Spain')

ggplot(data = pd,
       aes(x = date,
           y = cases_non_cum)) +
  geom_bar(stat = 'identity') +
  theme_simple() +
  labs(x = 'Fecha',
       y = 'Casos diarios',
       title = 'Casos confirmados nuevos')

Daily deahts in Spain

pd <- df_country %>%
  filter(country == 'Spain')

ggplot(data = pd,
       aes(x = date,
           y = deaths_non_cum)) +
  geom_bar(stat = 'identity') +
  theme_simple() +
  labs(x = 'Fecha',
       y = 'Muertes diarias',
       title = 'Muertes')

Daily cases world / population-adjusted

covid19::plot_day_zero(countries = c('Italy', 'Spain', 'US', 'Germany',
                                     'Canada', 'UK', 'Netherlands'
                                     ),
                       ylog = F,
                       day0 = 1,
                       cumulative = F,
                       calendar = T,
                       pop = T,
                       point_alpha = 0,
                       color_var = 'geo')

Cases per pop last week

pd <- df_country %>%
  left_join(world_pop %>% dplyr::select(iso, pop)) %>%
  group_by(country) %>%
  mutate(max_date = max(date)) %>%
  mutate(week_ago = max_date - 6) %>%
  # filter(date == max(date)) %>%
  filter(date >= week_ago, date <= max_date) %>%
  group_by(country) %>%
  summarise(y = sum(cases_non_cum),
            pop = dplyr::first(pop),
            date_range = paste0(min(date), ' - ', max(date)),
            yp = sum(cases_non_cum) / dplyr::first(pop) * 1000000) %>%
  ungroup %>%
  filter(pop > 1000000) %>%
  arrange(desc(yp)) %>%
  head(15) %>%
  mutate(country = ifelse(country == 'United Kingdom', 'UK', country))
pd$country <- factor(pd$country, levels = unique(pd$country))

ggplot(data = pd,
       aes(x = country,
           y = yp)) +
  geom_bar(stat = 'identity') +
  theme_simple() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 12)) +
  geom_text(aes(label = round(yp, digits = 0)),
            nudge_y = -50,
            color = 'white') +
  labs(x = '',
       y = 'Cases per 1,000,000 (last 7 days)',
       title = 'New confirmed COVID-19 cases per million population, last 7 days')

Deaths per pop last week

pd <- df_country %>%
  left_join(world_pop %>% dplyr::select(iso, pop)) %>%
  group_by(country) %>%
  mutate(max_date = max(date)) %>%
  mutate(week_ago = max_date - 6) %>%
  # filter(date == max(date)) %>%
  filter(date >= week_ago, date <= max_date) %>%
  group_by(country) %>%
  summarise(y = sum(deaths_non_cum),
            pop = dplyr::first(pop),
            date_range = paste0(min(date), ' - ', max(date)),
            yp = sum(deaths_non_cum) / dplyr::first(pop) * 1000000) %>%
  ungroup %>%
  filter(pop > 1000000) %>%
  arrange(desc(yp)) %>%
  head(10) %>%
  mutate(country = ifelse(country == 'United Kingdom', 'UK', country))
pd$country <- factor(pd$country, levels = unique(pd$country))

ggplot(data = pd,
       aes(x = country,
           y = yp)) +
  geom_bar(stat = 'identity') +
  theme_simple() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 12)) +
  geom_text(aes(label = round(yp, digits = 0)),
            nudge_y = -10,
            color = 'white') +
  labs(x = '',
       y = 'Deaths per 1,000,000 (last 7 days)',
       title = 'New confirmed COVID-19 deaths per million population, last 7 days')

Lombardia, Catalonia, Madrid

New cases in last week

pd <- esp_df %>%
  left_join(esp_pop %>% dplyr::select(ccaa, pop)) %>%
  mutate(country = 'Spain') %>%
  bind_rows(
    ita %>% left_join(ita_pop %>% dplyr::select(ccaa, pop)) %>% mutate(country = 'Italy')
  ) %>%
  bind_rows(
    df %>% filter(country == 'US') %>% mutate(ccaa = district) %>% left_join(regions_pop %>% dplyr::select(ccaa, pop)) %>% mutate(country = 'US')) %>%
  group_by(ccaa) %>%
  mutate(max_date = max(date)) %>%
  mutate(week_ago = max_date - 6) %>%
  # filter(date == max(date)) %>%
  filter(date >= week_ago, date <= max_date) %>%
  group_by(ccaa) %>%
  summarise(y = sum(cases_non_cum),
            country = dplyr::first(country),
            pop = dplyr::first(pop),
            date_range = paste0(min(date), ' - ', max(date))) %>%
  ungroup %>%
  mutate(yp = y / pop * 1000000) %>%
  ungroup %>%
  # filter(pop > 1000000) %>%
  arrange(desc(yp)) 

#Get country totals
pd %>%
  group_by(country) %>%
  summarise(y = sum(y, na.rm = T),
            pop = sum(pop, na.rm = T)) %>%
  ungroup %>%
  mutate(yp = y / pop * 1000000)

# A tibble: 3 x 4
  country      y       pop    yp
  <chr>    <dbl>     <dbl> <dbl>
1 Italy    21712  60491453  359.
2 Spain    35984  47026208  765.
3 US      203720 328239523  621.

library(knitr)
library(kableExtra)
pd <- pd %>%
  mutate(yp = round(yp, digits = 1)) %>%
  mutate(Rank = 1:nrow(pd)) %>%
  dplyr::select(Rank, 
                Región = ccaa,
                `Casos nuevos, 7 días` = y,
                Población = pop,
                `Casos nuevos 7 días por millón` = yp)
kable(pd) %>%
  kable_styling("striped", full_width = F) %>%
  column_spec(which(names(pd) == 'Casos nuevos 7 días por millón'), bold = T) %>%
  row_spec(which(pd$`Región` %in% esp_df$ccaa), bold = T, color = "white", background = "#D7261E")

Rank	Región	Casos nuevos, 7 días	Población	Casos nuevos 7 días por millón
1	Navarra	3604	654214	5508.9
2	La Rioja	1063	316798	3355.5
3	New York	57311	19453561	2946.0
4	New Jersey	24138	8882190	2717.6
5	Rhode Island	2425	1059361	2289.1
6	Connecticut	6434	3565287	1804.6
7	Massachusetts	11210	6892503	1626.4
8	Madrid	9949	6663394	1493.1
9	District of Columbia	972	705749	1377.3
10	CLM	2716	2032863	1336.0
11	CyL	3079	2399548	1283.2
12	Valle d’Aosta	161	126202	1275.7
13	Cataluña	7915	7675217	1031.2
14	Delaware	987	973764	1013.6
15	Piemonte	4215	4376000	963.2
16	South Dakota	817	884659	923.5
17	Maryland	4748	6045680	785.4
18	Louisiana	3507	4648794	754.4
19	Pennsylvania	9622	12801989	751.6
20	Illinois	9488	12671821	748.7
21	País Vasco	1584	2207776	717.5
22	Liguria	1073	1557000	689.1
23	Lombardia	6657	10040000	663.0
24	Trentino-Alto Adige	709	1070000	662.6
25	Michigan	6365	9986857	637.3
26	Georgia	6092	10617423	573.8
27	Cantabria	327	581078	562.7
28	Extremadura	585	1067710	547.9
29	Emilia-Romagna	2427	4453000	545.0
30	Mississippi	1570	2976149	527.5
31	Aragón	693	1319291	525.3
32	Ohio	5944	11689100	508.5
33	Indiana	3329	6732219	494.5
34	Iowa	1449	3155070	459.3
35	Nebraska	834	1934408	431.1
36	North Dakota	296	762062	388.4
37	Veneto	1876	4905000	382.5
38	Virginia	3243	8535519	379.9
39	Colorado	2039	5758736	354.1
40	New Hampshire	462	1359711	339.8
41	New Mexico	709	2096829	338.1
42	Galicia	871	2699499	322.7
43	Asturias	314	1022800	307.0
44	Abruzzo	399	1315000	303.4
45	Toscana	1117	3737000	298.9
46	Marche	445	1532000	290.5
47	Florida	6040	21477737	281.2
48	Alabama	1345	4903185	274.3
49	Nevada	840	3080156	272.7
50	Utah	850	3205958	265.1
51	California	9755	39512223	246.9
52	Friuli Venezia Giulia	293	1216000	241.0
53	Tennessee	1628	6829174	238.4
54	C. Valenciana	1186	5003769	237.0
55	Kentucky	1032	4467673	231.0
56	Kansas	658	2913314	225.9
57	Ceuta	19	84777	224.1
58	Missouri	1375	6137428	224.0
59	Baleares	237	1149460	206.2
60	South Carolina	1055	5148714	204.9
61	Washington	1479	7614893	194.2
62	North Carolina	2009	10488084	191.6
63	Texas	5476	28995881	188.9
64	Arizona	1363	7278717	187.3
65	Arkansas	563	3017804	186.6
66	Wisconsin	1071	5822434	183.9
67	Andalucía	1497	8414240	177.9
68	West Virginia	291	1792147	162.4
69	Oklahoma	611	3956971	154.4
70	Minnesota	849	5639632	150.5
71	Lazio	847	5897000	143.6
72	Murcia	209	1493898	139.9
73	Idaho	246	1787065	137.7
74	Maine	177	1344212	131.7
75	Puglia	502	4048000	124.0
76	Vermont	68	623989	109.0
77	Melilla	9	86487	104.1
78	Oregon	373	4217737	88.4
79	Molise	24	308493	77.8
80	Wyoming	42	578759	72.6
81	Campania	404	5827000	69.3
82	Sardegna	100	1648000	60.7
83	Alaska	44	731545	60.1
84	Sicilia	301	5027000	59.9
85	Canarias	127	2153389	59.0
86	Hawaii	80	1415872	56.5
87	Calabria	110	1957000	56.2
88	Basilicata	23	567118	40.6
89	Montana	39	1068778	36.5
90	Umbria	29	884640	32.8
91	American Samoa	0	NA	NA
92	Diamond Princess	0	NA	NA
93	Grand Princess	0	NA	NA
94	Guam	3	NA	NA
95	Northern Mariana Islands	3	NA	NA
96	Puerto Rico	349	NA	NA
97	Recovered	0	NA	NA
98	United States Virgin Islands	6	NA	NA
99	US	1	NA	NA
100	Virgin Islands	2	NA	NA
101	Wuhan Evacuee	4	NA	NA
102	NA	2	NA	NA

Just Italy and Spain

xpd = pd %>% filter(`Región` %in% c(ita$ccaa, esp_df$ccaa))
xpd$Rank <- 1:nrow(xpd)

kable(xpd) %>%
  kable_styling("striped", full_width = F) %>%
  column_spec(which(names(xpd) == 'Casos nuevos 7 días por millón'), bold = T) %>%
  row_spec(which(xpd$`Región` %in% esp_df$ccaa), bold = T, color = "white", background = "#D7261E")

Rank	Región	Casos nuevos, 7 días	Población	Casos nuevos 7 días por millón
1	Navarra	3604	654214	5508.9
2	La Rioja	1063	316798	3355.5
3	Madrid	9949	6663394	1493.1
4	CLM	2716	2032863	1336.0
5	CyL	3079	2399548	1283.2
6	Valle d’Aosta	161	126202	1275.7
7	Cataluña	7915	7675217	1031.2
8	Piemonte	4215	4376000	963.2
9	País Vasco	1584	2207776	717.5
10	Liguria	1073	1557000	689.1
11	Lombardia	6657	10040000	663.0
12	Trentino-Alto Adige	709	1070000	662.6
13	Cantabria	327	581078	562.7
14	Extremadura	585	1067710	547.9
15	Emilia-Romagna	2427	4453000	545.0
16	Aragón	693	1319291	525.3
17	Veneto	1876	4905000	382.5
18	Galicia	871	2699499	322.7
19	Asturias	314	1022800	307.0
20	Abruzzo	399	1315000	303.4
21	Toscana	1117	3737000	298.9
22	Marche	445	1532000	290.5
23	Friuli Venezia Giulia	293	1216000	241.0
24	C. Valenciana	1186	5003769	237.0
25	Ceuta	19	84777	224.1
26	Baleares	237	1149460	206.2
27	Andalucía	1497	8414240	177.9
28	Lazio	847	5897000	143.6
29	Murcia	209	1493898	139.9
30	Puglia	502	4048000	124.0
31	Melilla	9	86487	104.1
32	Molise	24	308493	77.8
33	Campania	404	5827000	69.3
34	Sardegna	100	1648000	60.7
35	Sicilia	301	5027000	59.9
36	Canarias	127	2153389	59.0
37	Calabria	110	1957000	56.2
38	Basilicata	23	567118	40.6
39	Umbria	29	884640	32.8

x = esp_df %>% left_join(esp_pop) %>%
  bind_rows(ita %>% left_join(ita_pop)) %>%
  filter(date == '2020-04-09') %>%
  filter(ccaa %in% c('Madrid',
                     'Cataluña', 'Lombardia')) %>%
  dplyr::select(ccaa, deaths, cases, pop) %>%
  mutate(deathsp = deaths / pop * 100000,
         casesp = cases / pop * 100000)


covid19::plot_day_zero(countries = c('Italy', 'Spain', 'China', 'South Korea', 'Sinagpore'),
                       districts = c('Madrid', #'Hubei',
                     'Cataluña', 'Lombardia'),
                     by_district = T,
                     roll = 7,
                     deaths = F,
                     pop = T,
                     day0 = 0,
                     ylog = F,
                     calendar = T,
                     cumulative = F) +
  labs(x = 'Data',
       y = 'Casos diaris (mitjana mòbil de 7 dies)',
       title = 'Casos diaris per 1.000.000',
       subtitle = 'Mitjana mòbil de 7 dies') 

covid19::plot_day_zero(countries = c('Italy', 'Spain'),
                     roll = 7,
                     deaths = F,
                     pop = T,
                     day0 = 0,
                     ylog = F,
                     calendar = T,
                     cumulative = F) +
  labs(x = 'Data',
       y = 'Casos diaris per 1.000.000 (mitjana mòbil de 7 dies)',
       title = 'Casos diaris per 1.000.000',
       subtitle = 'Mitjana mòbil de 7 dies') +
  theme(legend.direction = 'horizontal',
        legend.position = 'top')

covid19::plot_day_zero(countries = c('Italy', 'Spain'),
                     roll = 7,
                     deaths = T,
                     pop = T,
                     day0 = 0,
                     ylog = F,
                     calendar = T,
                     cumulative = F) +
  labs(x = 'Data',
       y = 'Morts diaris per 1.000.000 (mitjana mòbil de 7 dies)',
       title = 'Morts diaris per 1.000.000',
       subtitle = 'Mitjana mòbil de 7 dies') +
  theme(legend.direction = 'horizontal',
        legend.position = 'top')

covid19::plot_day_zero(countries = c('Italy', 'Spain'),
                       by_district = T,
                       districts = c('Madrid', 'Emilia-Romagna',
                     'Cataluña', 'Lombardia'),
                     roll = 7,
                     deaths = F,
                     pop = T,
                     day0 = 0,
                     ylog = F,
                     calendar = T,
                     cumulative = F) +
  labs(x = 'Data',
       y = 'Casos diaris per 1.000.000 (mitjana mòbil de 7 dies)',
       title = 'Casos diaris per 1.000.000',
       subtitle = 'Mitjana mòbil de 7 dies') +
  theme(legend.direction = 'horizontal',
        legend.position = 'top')

Personas susceptibles

left <- esp_df %>%
  group_by(date) %>%
  summarise(cases = sum(cases))
left$pop <- esp_pop %>% summarise(pop = sum(pop)) %>% .$pop
left$susceptible <- left$pop - (left$cases * 10)
left$p <- left$susceptible / left$pop * 100

ggplot(data = left,
       aes(x = date,
           y = p)) +
  geom_line() +
  ylim(0, 100)

Asia

covid19::plot_day_zero(countries = c('Japan', 'South Korea', 'Singapore', 'Hong Kong'),
                     roll = 7,
                     deaths = F,
                     # pop = T,
                     day0 = 0,
                     ylog = F,
                     calendar = T,
                     cumulative = F) +
    labs(x = 'Data',
       y = 'Casos diaris (mitjana mòbil de 3 dies)',
       title = 'Casos diaris',
       subtitle = 'Mitjana mòbil de 3 dies') +
  facet_wrap(~geo, scales = 'free_y') +
  theme(legend.position = 'none')

Day of week analysis

pd <- esp_df %>%
  arrange(date) %>%
  group_by(date) %>%
  summarise(deaths_non_cum = sum(deaths_non_cum),
            cases_non_cum = sum(cases_non_cum)) %>%
  ungroup %>%  
  mutate(dow = weekdays(date)) %>%
  mutate(week = isoweek(date)) %>%
  group_by(week) %>%
  mutate(start_date = min(date)) %>%
  ungroup %>%
  filter(date >= '2020-03-09')
pd$dow <- factor(pd$dow,
                 levels = c('Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday',
                            'Saturday', 'Sunday'),
                 labels = c('Lunes', 'Martes', 'Miércoles', 'Jueves', 'Viernes',
                            'Sábado', 'Domingo'))
n_cols <- length(unique(pd$start_date))
cols <- colorRampPalette(RColorBrewer::brewer.pal(n = 9, name = 'Spectral'))(n_cols)
pd$start_date <- factor(pd$start_date)
ggplot(data = pd,
       aes(x = dow,
           y = cases_non_cum,
           group = week,
           color = start_date)) +
  geom_line(size = 4) +
  geom_point(size = 4) +
  scale_color_manual(name = 'Primer día\nde la semana',
                     values = cols) +
  theme_simple() +
  labs(x = 'Día de la semana',
       y = 'Muertes')

Basque country vs rest of Spain

pd <- esp_df %>%
  mutate(geo = ifelse(ccaa == 'País Vasco', 'Basque country', 'Rest of Spain')) %>%
  group_by(geo, date) %>%
  summarise(deaths = sum(deaths)) %>%
  ungroup
pp <- esp_pop %>%
    mutate(geo = ifelse(ccaa == 'País Vasco', 'Basque country', 'Rest of Spain')) %>%
  group_by(geo) %>%
  summarise(pop = sum(pop))
pd <- left_join(pd, pp) %>% mutate(pk = deaths / pop * 100000)

ggplot(data = pd %>% filter(pk > 0.1),
       aes(x = date,
           y = pk,
           color = geo)) +
  geom_line() +
  labs(x = 'Date',
       y = 'Cumulative deaths per 100,000') +
  scale_color_manual(name = '',
                     values = c('red', 'black')) +
  theme_simple() 

Country trajectories, population adjusted

countries <- c(
  'Spain',
  'US',
  'France',
  # 'Portugal',
  'Italy',
  'China'
)
districts <- c('Lombardia', 'Cataluña', 
               'New York', 
               # 'Hubei',
               'CyL', 
               'CLM', 
               # 'Washington',
               'La Rioja',
               'Madrid')

plot_day_zero(countries = countries,
              districts = districts,
              ylog = F,
              day0 = 1,
              cumulative = F,
              time_before = 0,
              roll = 3,
              deaths = T,
              pop = T,
              by_district = T,
              point_alpha = 0,
              line_size = 3,
              color_var = 'geo')

Italy and Spain

dir.create('/tmp/totesmou')
plot_day_zero(countries = c('Spain', 'Italy', max_date = Sys.Date()-1),
              point_size = 2, calendar = T)

ggsave('/tmp/totesmou/1_italy_vs_spain.png',
       height = 5.6,
       width = 9.6)

plot_day_zero(countries = c('Spain', 'Italy'),
              point_size = 2, calendar = F)

ggsave('/tmp/totesmou/2_italy_vs_spain_temps_ajustat.png',
       height = 5.6,
       width = 9.6)

plot_day_zero(countries = c('Spain', 'Italy'),
              point_size = 2, calendar = T, deaths = T, day0 = 10)

plot_day_zero(countries = c('Spain', 'Italy'),
              point_size = 2, calendar = F, deaths = T, day0 = 10)

plot_day_zero(countries = c('Spain', 'Italy'),
              point_size = 2, calendar = F, deaths = T, day0 = 10, pop = T)

plot_day_zero(countries = c('Spain', 'Italy'),
              point_size = 2, calendar = F, deaths = T, day0 = 1, pop = T, roll = 3, roll_fun = 'mean')

plot_day_zero(countries = c('Spain', 'Italy'),
              districts = c('Cataluña', 'Madrid',
                            'Lombardia', 'Emilia-Romagna'),
              by_district = T,
              day0 = 10,
              pop = T)

plot_day_zero(countries = c('Spain', 'Italy'),
              districts = c('Cataluña', 'Madrid',
                            'Lombardia', 'Emilia-Romagna'),
              by_district = T,
              deaths = T,
              day0 = 1,
              pop = T)

plot_day_zero(countries = c('Spain', 'Italy'),
              districts = c('Cataluña', 'Madrid',
                            'Lombardia', 'Emilia-Romagna'),
              by_district = T,
              deaths = T,
              roll = 3,
              roll_fun = 'mean',
              day0 = 1,
              ylog = F,
              pop = T, calendar = T)

plot_day_zero(countries = c('Spain', 'Italy', 'US'),
              districts = c('Cataluña', 'Madrid', 
                            'New York', 
                            'Lombardia', 'Emilia-Romagna'),
              by_district = T,
              deaths = T,
              roll = 3,
              roll_fun = 'mean',
              day0 = 1,
              pop = T)

plot_day_zero(countries = c('Spain', 'Italy', 'US'),
              districts = c('Cataluña', 'Madrid', 
                            'New York', 
                            'Lombardia', 'Emilia-Romagna'),
              by_district = T,
              deaths = T,
              # roll = 7,
              roll_fun = 'mean',
              day0 = 1,
              pop = F)

plot_day_zero(color_var = 'iso', by_district = T,
              deaths = T,
              day0 = 1,
              alpha = 0.6,
              point_alpha = 0,
              calendar = T,
              countries = c('Spain', 'Italy'),
              pop = T)

place_transform <- function(x){ifelse(x == 'Madrid', 'Madrid',
                                      # ifelse(x == 'Cataluña', 'Cataluña',
                                             'Altres CCAA')
  # )
}
place_transform_ita <- function(x){
  ifelse(x == 'Lombardia', 'Lombardia', 
         # ifelse(x == 'Emilia Romagna', 'Emilia Romagna', 
                'Altres regions italianes')
  # )
}
pd <- esp_df %>% mutate(country = 'Espanya') %>%
  mutate(ccaa = place_transform(ccaa)) %>%
  bind_rows(ita %>% mutate(ccaa = place_transform_ita(ccaa),
                           country = 'Itàlia')) %>%
  group_by(country, ccaa, date) %>% 
  summarise(cases = sum(cases),
            uci = sum(uci),
            deaths = sum(deaths),
            cases_non_cum = sum(cases_non_cum),
            deaths_non_cum = sum(deaths_non_cum),
            uci_non_cum = sum(uci_non_cum)) %>%
  left_join(esp_pop %>%
              mutate(ccaa = place_transform(ccaa)) %>%
              bind_rows(ita_pop %>% mutate(ccaa = place_transform_ita(ccaa))) %>%
              group_by(ccaa) %>%
              summarise(pop = sum(pop))) %>%
  mutate(deaths_non_cum_p = deaths_non_cum / pop * 1000000) %>%
  group_by(country, date) %>%
  mutate(p_deaths_non_cum_country = deaths_non_cum / sum(deaths_non_cum) * 100,
         p_deaths_country = deaths / sum(deaths) * 100)
pd$ccaa <- factor(pd$ccaa,
                  levels = c('Madrid',
                             # 'Cataluña',
                             'Altres CCAA',
                             'Lombardia',
                             # 'Emilia Romagna',
                             'Altres regions italianes'))
cols <- c(
  rev(brewer.pal(n = 3, 'Reds'))[1:2],
  rev(brewer.pal(n = 3, 'Blues'))[1:2]
)

label_data <- pd %>%
  filter(country  %in% c('Itàlia', 'Espanya')) %>%
  group_by(country) %>%
  filter(date == max(date))  %>%
  mutate(label = gsub('\nitalianas', '',  gsub(' ', '\n', ccaa))) %>%
  mutate(x = date - 2,
         y = p_deaths_country + 
           ifelse(p_deaths_country > 50, 10, -9))
ggplot(data = pd %>% group_by(country) %>% mutate(start_day = dplyr::first(date[deaths >=50])) %>% filter(date >= start_day),
       aes(x = date,
           y = p_deaths_country,
           color = ccaa,
           group = ccaa)) +
  # geom_bar(stat = 'identity',
  #          position = position_dodge(width = 0.99)) +
  scale_fill_manual(name = '', values = cols) +
  scale_color_manual(name = '', values = cols) +
  geom_line(size = 2,
            aes(color = ccaa)) +
  geom_point(size = 3,
             aes(color = ccaa)) +
  facet_wrap(~country) +
  # xlim(as.Date('2020-03-09'),
  #      Sys.Date()-1) +
  theme_simple() +
  geom_hline(yintercept = 50, lty = 2, alpha = 0.6) +
  # geom_line(lty = 2, alpha = 0.6) +
  labs(x = 'Data',
       y = 'Percentatge de morts',
       title = 'Percentatge de morts: regió més afectada vs resta del pais',
       subtitle = 'A partir del primer día a cada país amb 50 morts acumulades') +
  theme(legend.position = 'top',
        strip.text = element_text(size = 30),
        legend.text = element_text(size = 10))  +
  guides(color = guide_legend(nrow = 2,
                              keywidth = 5)) +
  geom_text(data = label_data,
            aes(x = x-2,
                y = y,
                label = label,
                color = ccaa),
            size = 7,
            show.legend = FALSE)

# Same cahrt as previous, but one shared axis
place_transform <- function(x){ifelse(x == 'Madrid', 'Madrid',
                                      # ifelse(x == 'Cataluña', 'Cataluña',
                                             'Rest of Spain')
  # )
}
place_transform_ita <- function(x){
  ifelse(x == 'Lombardia', 'Lombardia', 
         # ifelse(x == 'Emilia Romagna', 'Emilia Romagna', 
                'Rest of Italy')
  # )
}
pd <- esp_df %>% mutate(country = 'Spain') %>%
  mutate(ccaa = place_transform(ccaa)) %>%
  bind_rows(ita %>% mutate(ccaa = place_transform_ita(ccaa),
                           country = 'Italy')) %>%
  group_by(country, ccaa, date) %>% 
  summarise(cases = sum(cases),
            uci = sum(uci),
            deaths = sum(deaths),
            cases_non_cum = sum(cases_non_cum),
            deaths_non_cum = sum(deaths_non_cum),
            uci_non_cum = sum(uci_non_cum)) %>%
  left_join(esp_pop %>%
              mutate(ccaa = place_transform(ccaa)) %>%
              bind_rows(ita_pop %>% mutate(ccaa = place_transform_ita(ccaa))) %>%
              group_by(ccaa) %>%
              summarise(pop = sum(pop))) %>%
  mutate(deaths_non_cum_p = deaths_non_cum / pop * 1000000) %>%
  group_by(country, date) %>%
  mutate(p_deaths_non_cum_country = deaths_non_cum / sum(deaths_non_cum) * 100,
         p_deaths_country = deaths / sum(deaths) * 100)
pd$ccaa <- factor(pd$ccaa,
                  levels = c('Madrid',
                             # 'Cataluña',
                             'Rest of Spain',
                             'Lombardia',
                             # 'Emilia Romagna',
                             'Rest of Italy'))
cols <- c(
  rev(brewer.pal(n = 3, 'Reds'))[1:2],
  rev(brewer.pal(n = 3, 'Blues'))[1:2]
)

label_data <- pd %>%
  filter(country  %in% c('Italy', 'Spain')) %>%
  group_by(country) %>%
  filter(date == max(date))  %>%
  # mutate(label = gsub('\nitalianas', '',  gsub(' ', '\n', ccaa))) %>%
  mutate(x = date - 2,
         y = p_deaths_country + 
           ifelse(p_deaths_country > 50, 10, -9)) %>%
  ungroup
ggplot(data = pd %>% group_by(country) %>% mutate(start_day = dplyr::first(date[deaths >=30])) %>% filter(date >= start_day),
       aes(x = date,
           y = p_deaths_country,
           color = ccaa,
           group = ccaa)) +
  # geom_bar(stat = 'identity',
  #          position = position_dodge(width = 0.99)) +
  scale_fill_manual(name = '', values = cols) +
  scale_color_manual(name = '', values = cols) +
  geom_line(size = 2,
            aes(color = ccaa)) +
  geom_point(size = 3,
             aes(color = ccaa)) +
  # facet_wrap(~country, scales = 'free_x') +
  # xlim(as.Date('2020-03-09'),
  #      Sys.Date()-1) +
  theme_simple() +
  geom_hline(yintercept = 50, lty = 2, alpha = 0.6) +
  # geom_line(lty = 2, alpha = 0.6) +
  labs(x = 'Date',
       y = 'Percentage of deaths',
       title = 'Percentage of country\'s cumulative COVID-19 deaths by geography',
       subtitle = 'Starting at first day with 50 or more cumulative deaths') +
  theme(legend.position = 'top',
        strip.text = element_text(size = 30),
        legend.text = element_text(size = 10))  +
  guides(color = guide_legend(nrow = 2,
                              keywidth = 5))# +

  # geom_text(data = label_data,
  #           aes(x = x-2,
  #               y = y,
  #               label = label,
  #               color = ccaa),
  #           size = 7,
  #           show.legend = FALSE)

Same chart as above but absolute numbers

# Same cahrt as previous, but one shared axis
place_transform <- function(x){ifelse(x == 'Madrid', 'Madrid',
                                      # ifelse(x == 'Cataluña', 'Cataluña',
                                             'Rest of Spain')
  # )
}
place_transform_ita <- function(x){
  ifelse(x == 'Lombardia', 'Lombardia', 
         # ifelse(x == 'Emilia Romagna', 'Emilia Romagna', 
                'Rest of Italy')
  # )
}
pd <- esp_df %>% mutate(country = 'Spain') %>%
  mutate(ccaa = place_transform(ccaa)) %>%
  bind_rows(ita %>% mutate(ccaa = place_transform_ita(ccaa),
                           country = 'Italy')) %>%
  group_by(country, ccaa, date) %>% 
  summarise(cases = sum(cases),
            uci = sum(uci),
            deaths = sum(deaths),
            cases_non_cum = sum(cases_non_cum),
            deaths_non_cum = sum(deaths_non_cum),
            uci_non_cum = sum(uci_non_cum)) %>%
  left_join(esp_pop %>%
              mutate(ccaa = place_transform(ccaa)) %>%
              bind_rows(ita_pop %>% mutate(ccaa = place_transform_ita(ccaa))) %>%
              group_by(ccaa) %>%
              summarise(pop = sum(pop))) %>%
  mutate(deaths_non_cum_p = deaths_non_cum / pop * 1000000) %>%
  group_by(country, date) %>%
  mutate(p_deaths_non_cum_country = deaths_non_cum / sum(deaths_non_cum) * 100,
         p_deaths_country = deaths / sum(deaths) * 100)
pd$ccaa <- factor(pd$ccaa,
                  levels = rev(c('Madrid',
                             # 'Cataluña',
                             'Rest of Spain',
                             'Lombardia',
                             # 'Emilia Romagna',
                             'Rest of Italy')))
cols <- c(
  rev(brewer.pal(n = 3, 'Reds'))[1:2],
  rev(brewer.pal(n = 3, 'Blues'))[1:2]
)

label_data <- pd %>%
  filter(country  %in% c('Italy', 'Spain')) %>%
  group_by(country) %>%
  filter(date == max(date))  %>%
  # mutate(label = gsub('\nitalianas', '',  gsub(' ', '\n', ccaa))) %>%
  mutate(x = date - 2,
         y = p_deaths_country + 
           ifelse(p_deaths_country > 50, 10, -9)) %>%
  ungroup

# Get moving
ma <- function(x, n = 2){
    
    if(length(x) >= n){
      stats::filter(x, rep(1 / n, n), sides = 1)
    } else {
      new_n <- length(x)
      stats::filter(x, rep(1 / new_n, new_n), sides = 1)
    }
}


ggplot(data = pd %>% group_by(country) %>% 
         mutate(start_day = dplyr::first(date[deaths >=1])) %>% 
         filter(date >= start_day) %>% 
         mutate(days_since = as.numeric(date - start_day)) %>%
         ungroup %>% arrange(date) %>%
         group_by(country, ccaa) %>%
         mutate(rolling_average = ma(deaths_non_cum, n = 3)) %>%
         ungroup,
       aes(x = date,
           y = rolling_average,
           color = ccaa,
           group = ccaa)) +
  # geom_bar(stat = 'identity',
  #          position = position_dodge(width = 0.99)) +
  scale_fill_manual(name = '', values = cols) +
  scale_color_manual(name = '', values = cols) +
  geom_line(size = 2,
            aes(color = ccaa)) +
  geom_point(size = 3,
             aes(color = ccaa)) +
  # scale_y_log10(limits = c(1.5, 1000)) +
  # scale_y_log10() +
  facet_wrap(~country) +
  # xlim(as.Date('2020-03-09'),
  #      Sys.Date()-1) +
  theme_simple() +
  # geom_hline(yintercept = 50, lty = 2, alpha = 0.6) +
  # geom_line(lty = 2, alpha = 0.6) +
  labs(x = 'Date',
       y = 'Deaths (log-scale)',
       title = 'Daily COVID-19 deaths by geography',
       subtitle = '3-day rolling average') +
  theme(legend.position = 'top',
        plot.title = element_text(size = 30),
        plot.subtitle = element_text(size = 24),
        strip.text = element_text(size = 30, hjust = 0.5),
        legend.text = element_text(size = 20))  +
  guides(color = guide_legend(nrow = 2,
                              keywidth = 5))

ggsave('~/Desktop/madlom.png')

roll_curve <- function(data,
                       n = 4,
                       deaths = FALSE,
                       scales = 'fixed',
                       nrow = NULL,
                       ncol = NULL,
                       pop = FALSE){

  # Create the n day rolling avg
  ma <- function(x, n = 5){
    
    if(length(x) >= n){
      stats::filter(x, rep(1 / n, n), sides = 1)
    } else {
      new_n <- length(x)
      stats::filter(x, rep(1 / new_n, new_n), sides = 1)
    }
    
    
  }
  
  pd <- data
  if(deaths){
    pd$var <- pd$deaths_non_cum
  } else {
    pd$var <- pd$cases_non_cum
  }
  
  if('ccaa' %in% names(pd)){
    pd$geo <- pd$ccaa
  } else {
    pd$geo <- pd$iso
  }
  
  if(pop){
    # try to get population
    if(any(pd$geo %in% unique(esp_df$ccaa))){
      right <- esp_pop
      right_var <- 'ccaa'
    } else {
      right <- world_pop
      right_var <- 'iso'
    }
    pd <- pd %>% left_join(right %>% dplyr::select(all_of(right_var), pop),
                           by = c('geo' = right_var)) %>%
      mutate(var = var / pop * 100000)
  }
  
  pd <- pd %>%
    arrange(date) %>%
    group_by(geo) %>%
    mutate(with_lag = ma(var, n = n))
  
  
  ggplot() +
    geom_bar(data = pd,
         aes(x = date,
             y = var),
             stat = 'identity',
         fill = 'grey',
         alpha = 0.8) +
    geom_area(data = pd,
              aes(x = date,
                  y = with_lag),
              color = 'red',
              fill = 'red',
              alpha = 0.6) +
    facet_wrap(~geo, scales = scales, nrow = nrow, ncol = ncol) +
    theme_simple() +
    labs(x = '',
         y = ifelse(deaths, 'Deaths', 'Cases'),
         title = paste0('Daily (non-cumulative) ', ifelse(deaths, 'deaths', 'cases'),
                        ifelse(pop, ' (per 100,000)', '')),
         subtitle = paste0('Data as of ', max(data$date),
                           '.\nRed line = ', n, ' day rolling average. Grey bar = day-specific value.')) +
    theme(axis.text.x = element_text(size = 7,
                                     angle = 90,
                                     hjust = 0.5, vjust = 1)) #+
    # scale_x_date(name ='',
    #              breaks = sort(unique(pd$date)),
    #              labels = format(sort(unique(pd$date)), '%b %d'))
    # scale_y_log10()
}
this_ccaa <- 'Cataluña'
plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(ccaa == this_ccaa)
roll_curve(plot_data, scales = 'fixed')  + theme(strip.text = element_text(size = 30))

plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(ccaa == this_ccaa)
roll_curve(plot_data, deaths = T, scales = 'fixed') + theme(strip.text = element_text(size = 30))

african_countries <-  world_pop$country[world_pop$sub_region %in% c('Sub-Saharan Africa')]

pd <- plot_day_zero(countries = c(african_countries),
                    day0 = 10,
                    max_date = Sys.Date() - 13)
pd

pd <- plot_day_zero(countries = c(african_countries),
                    day0 = 10,
                    max_date = Sys.Date() - 6)
pd

pd <- plot_day_zero(countries = c(african_countries),
                    day0 = 10,
                    max_date = Sys.Date(),
                    ylog = F)
pd

latam_countries <-  world_pop$country[world_pop$sub_region %in% c('Latin America and the Caribbean')]
latam_countries <- latam_countries[!latam_countries %in% c('Guyana')]

pd <- plot_day_zero(countries = c(latam_countries),
                    day0 = 10,
                    max_date = Sys.Date() - 13)
pd

pd <- plot_day_zero(countries = c(latam_countries),
                    day0 = 10,
                    max_date = Sys.Date() - 6)
pd

pd <- plot_day_zero(countries = c(latam_countries),
                    day0 = 10,
                    max_date = Sys.Date())
pd

Latin America and Africa vs Europe

isos <- sort(unique(world_pop$sub_region))
keep_countries <- world_pop$country[world_pop$sub_region %in% c('Latin America and the Caribbean', 'Sub-Saharan Africa') |
                                      world_pop$region %in% 'Europe']
keep_countries <- keep_countries[!keep_countries %in% c('Guyana')]
pd <- df_country %>% ungroup %>%
  filter(country %in% keep_countries) %>%
  dplyr::select(-country) %>%
  left_join(world_pop) %>%
  group_by(iso) %>%
  mutate(day0 = min(date[cases >= 10])) %>%
  ungroup %>%
  mutate(days_since = date - day0) %>%
  filter(days_since >= 0)


cols <- c( 'black')
g <- ggplot(data = pd,
       aes(x = days_since,
           y = cases,
           group = country,
           color = region)) +
  geom_line(data = pd %>% filter(region == 'Europe'),
            alpha = 0.6) +
  scale_y_log10() +
  scale_color_manual(name = '', values = cols) +
  theme_simple() +
  labs(x = 'Days since first day at 10 cases') +
  theme(legend.position = 'top')
g

cols <- c('darkred', 'black')

g + 
    geom_line(data = pd %>% filter(region == 'Africa'),
            # alpha = 1,
            size = 1.5,
            alpha = 0.8) +
    scale_color_manual(name = '', values = cols) +
  scale_y_continuous()

cols <- c( 'darkorange', 'black')
g + 
    geom_line(data = pd %>% filter(sub_region == 'Latin America and the Caribbean'),
            # alpha = 1,
            size = 1.5,
            alpha = 0.8) +
    scale_color_manual(name = '', values = cols) 

cols <- c('darkred', 'darkorange', 'black')
g + 
    geom_line(data = pd %>% filter(sub_region != 'Europe'),
            # alpha = 1,
            size = 1.5,
            alpha = 0.8) +
    scale_color_manual(name = '', values = cols) 

# Assets
pyramid_dir <- '../../data-raw/pyramids/'
pyramid_files <- dir(pyramid_dir)
out_list <- list()
for(i in 1:length(pyramid_files)){
  out_list[[i]] <- read_csv(paste0(pyramid_dir, pyramid_files[i])) %>%
    mutate(region = gsub('.csv', '', pyramid_files[i]))
}
pyramid <- bind_rows(out_list)
make_pyramid <- function(the_region = 'AFRICA-2019'){
  sub_data <- pyramid %>% filter(region == the_region)
  sub_data$Age <- factor(sub_data$Age, levels = sub_data$Age)
  sub_data <- tidyr::gather(sub_data, key, value, M:F)
  ggplot(data = sub_data,
         aes(x = Age,
             y = value,
             fill = key)) +
    geom_bar(stat = 'identity',
             position = position_dodge()) +
    scale_fill_manual(name = '', values = c('darkgrey', 'lightblue')) +
    theme_simple() +
    labs(x = 'Age group',
         y = 'Population') +
    theme(legend.position = 'top')
}
make_pyramid_overlay <- function(){
  sub_data <- pyramid %>% filter(region %in% c('EUROPE-2019',
                                               'AFRICA-2019',
                                               'LATIN AMERICA AND THE CARIBBEAN-2019')) %>%
    mutate(region = gsub('-2019', '', region))
   sub_data$Age <- factor(sub_data$Age, levels = unique(sub_data$Age))
  sub_data <- tidyr::gather(sub_data, key, value, M:F) %>%
    group_by(Age, region) %>%
    summarise(value = sum(value)) %>%
    ungroup %>%
    group_by(region) %>%
    mutate(p = value / sum(value) * 100) %>%
    ungroup
  ggplot(data = sub_data,
         aes(x = Age,
             y = p,
             color = region,
             group = region,
             fill = region)) +
    geom_area(position = position_dodge(),
              alpha = 0.6) +
    scale_fill_manual(name = '',
                      values = c('darkred', 'darkorange', 'black')) +
    scale_color_manual(name = '',
                      values = c('darkred', 'darkorange', 'black')) +
    theme_simple() +
    theme(legend.position = 'top') +
    labs(x = 'Age group', y = 'Percentage')
}

make_pyramid(the_region = 'Spain-2019') + labs(title = 'Spain')
make_pyramid(the_region = 'Italy-2019') + labs(title = 'Italy')

make_pyramid(the_region = 'EUROPE-2019') + labs(title = 'Europe')
make_pyramid(the_region = 'Kenya-2019') + labs(title = 'Kenya')


make_pyramid(the_region = 'Guatemala-2019') + labs(title = 'Guatemala')

make_pyramid(the_region = 'AFRICA-2019') + labs(title = 'Africa')

make_pyramid(the_region = 'LATIN AMERICA AND THE CARIBBEAN-2019') + labs(title = 'Latin America and the Caribbean')

make_pyramid_overlay() + labs(title = 'Population distribution by region')

Pics

plot_day_zero(countries = c('China', 'Italy', 'Spain'),
              districts = c('Hubei', 'Lombardia', 'Cataluña', 'Madrid'),
              by_district = T,
              point_alpha = 0,
              day0 = 5,
              pop = F,
              deaths = T,
              ylog = T,
              calendar = F,
              roll = 5)

Map of portugal, france, spain

# cat_transform <- function(x){ifelse(x == 'Catalunya', 'Cataluña', x)}
cat_transform <- function(x){return(x)}
pd <- por_df %>% mutate(country = 'Portugal') %>%
  bind_rows(esp_df %>% mutate(country = 'Spain')) %>%
  bind_rows(fra_df %>% mutate(country = 'France')) %>%
  bind_rows(ita %>% mutate(country = 'Italy')) %>%
  bind_rows(
    df %>% filter(country == 'Andorra') %>%
      mutate(ccaa = 'Andorra')
  )
keep_date = pd %>% group_by(country) %>% summarise(max_date= max(date)) %>% summarise(x = min(max_date)) %>% .$x
pd <- pd %>%
  mutate(ccaa = cat_transform(ccaa)) %>%
  group_by(ccaa) %>%
  filter(date == keep_date) %>%
  # filter(date == '2020-03-27') %>%
  ungroup %>%
  dplyr::select(date, ccaa, deaths, deaths_non_cum,
                cases, cases_non_cum) %>%
  left_join(regions_pop %>%
              bind_rows(
                world_pop %>% filter(country == 'Andorra') %>% dplyr::mutate(ccaa = country) %>%
                  dplyr::select(-region, -sub_region)
              )) %>%
  mutate(cases_per_million = cases / pop * 1000000,
         deaths_per_million = deaths / pop * 1000000) %>%
  mutate(cases_per_million_non_cum = cases_non_cum / pop * 1000000,
         deaths_per_million_non_cum = deaths_non_cum / pop * 1000000)

map_esp1 <- map_esp
map_esp1@data <- map_esp1@data %>% dplyr::select(ccaa)
map_fra1 <- map_fra
map_fra1@data <- map_fra1@data %>% dplyr::select(ccaa = NAME_1)
map_por1 <- map_por
map_por1@data <- map_por1@data %>% dplyr::select(ccaa = CCDR)
map_ita1 <- map_ita 
map_ita1@data <- map_ita1@data %>% dplyr::select(ccaa)
map_and1 <- map_and
map_and1@data <- map_and1@data %>% dplyr::select(ccaa = NAME_0)


map <- 
  rbind(map_esp1,
        map_por1,
        map_fra1,
        map_ita1,
        map_and1)

# Remove areas not of interest
centroids <- coordinates(map)
centroids <- data.frame(centroids)
names(centroids) <- c('x', 'y')
centroids$ccaa <- map@data$ccaa
centroids <- left_join(centroids, pd)
# map <- map_sp <- map[centroids$y >35 & centroids$x > -10 &
#              centroids$x < 8 & (centroids$y < 43  | map@data$ccaa %in% c('Occitanie', 'Nouvelle-Aquitaine') |
#                                   map@data$ccaa %in% esp_df$ccaa),]
# map_sp <- map <-  map[centroids$x > -10 & centroids$y <47,]
map_sp <- map <-  map[centroids$x > -10 & centroids$y <77,]

# map_sp <- map <-  map[centroids$x > -10,]

# fortify
map <- fortify(map, region = 'ccaa')

# join data
map$ccaa <- map$id
map <- left_join(map, pd)
var <- 'deaths_per_million'
map$var <- as.numeric(unlist(map[,var]))
centroids <- centroids[,c('ccaa', 'x', 'y', var)]
centroids <- centroids %>%
  filter(ccaa %in% map_sp@data$ccaa)

# cols <- rev(RColorBrewer::brewer.pal(n = 9, name = 'Spectral'))
# cols <- c('#A16928','#bd925a','#d6bd8d','#edeac2','#b5c8b8','#79a7ac','#2887a1')
# cols <- c('#009392','#39b185','#9ccb86','#e9e29c','#eeb479','#e88471','#cf597e')
# cols <- c('#008080','#70a494','#b4c8a8','#f6edbd','#edbb8a','#de8a5a','#ca562c')
cols <- rev(colorRampPalette(c('darkred', 'red', 'darkorange', 'orange', 'yellow', 'lightblue'))(10))
g <- ggplot(data = map,
         aes(x = long,
             y = lat,
             group = group)) +
    geom_polygon(aes(fill = var),
                 lwd = 0.3,
                 # color = 'darkgrey',
                 color = NA,
                 size = 0.6) +
      scale_fill_gradientn(name = '',
                           colours = cols) +
  # scale_fill_() +
  ggthemes::theme_map() +
  theme(legend.position = 'bottom',
        plot.title = element_text(size = 16)) +
  guides(fill = guide_colorbar(direction= 'horizontal',
                               barwidth = 50,
                               barheight = 1,
                               label.position = 'bottom')) +
  labs(title = 'Cumulative COVID-19 deaths per million population, western Mediterranean',
       subtitle = paste0('Data as of ', format(max(pd$date), '%B %d, %Y'))) +
  geom_text(data = centroids,
            aes(x = x,
                y = y,
                group = NA,
                label = paste0(ccaa, '\n',
                               round(deaths_per_million, digits = 2))),
            alpha = 0.8,
            size = 3)
g

ggsave('/tmp/map_with_borders.png',
       height = 8, width = 13)

Animation, Portugal, France, Spain, Italy

dir.create('/tmp/animation_map/')
pd <- por_df %>% mutate(country = 'Portugal') %>%
  bind_rows(esp_df %>% mutate(country = 'Spain')) %>%
  bind_rows(fra_df %>% mutate(country = 'France')) %>%
  bind_rows(ita %>% mutate(country = 'Italy')) %>%
  bind_rows(
    df %>% filter(country == 'Andorra') %>%
      mutate(ccaa = 'Andorra')
  )
pd %>% group_by(country) %>% summarise(max_date= max(date))

# A tibble: 5 x 2
  country  max_date  
  <chr>    <date>    
1 Andorra  2020-04-20
2 France   2020-04-20
3 Italy    2020-04-20
4 Portugal 2020-04-20
5 Spain    2020-04-20

unique_dates <- sort(unique(pd$date))
unique_dates <- unique_dates[unique_dates >= '2020-03-01']
popper <- regions_pop %>%
                bind_rows(
                  world_pop %>% filter(country == 'Andorra') %>% dplyr::mutate(ccaa = country) %>%
                    dplyr::select(-region, -sub_region)
                )


map_esp1 <- map_esp
map_esp1@data <- map_esp1@data %>% dplyr::select(ccaa)
map_fra1 <- map_fra
map_fra1@data <- map_fra1@data %>% dplyr::select(ccaa = NAME_1)
map_por1 <- map_por
map_por1@data <- map_por1@data %>% dplyr::select(ccaa = CCDR)
map_ita1 <- map_ita 
map_ita1@data <- map_ita1@data %>% dplyr::select(ccaa)
map_and1 <- map_and
map_and1@data <- map_and1@data %>% dplyr::select(ccaa = NAME_0)


map <- 
  rbind(map_esp1,
        map_por1,
        map_fra1,
        map_ita1,
        map_and1)

# Remove areas not of interest
centroids <- coordinates(map)
centroids <- data.frame(centroids)
names(centroids) <- c('x', 'y')
centroids$ccaa <- map@data$ccaa
# map <- map_sp <- map[centroids$y >35 & centroids$x > -10 &
#              # centroids$x < 8 &
#                (centroids$y < 43  | map@data$ccaa %in% c('Occitanie', 'Nouvelle-Aquitaine') |
#                                   map@data$ccaa %in% esp_df$ccaa),]
# map_sp <- map <-  map[centroids$x > -10 & centroids$y <47,]
map_sp <- map <-  map[centroids$x > -10 & centroids$y <477,]


# fortify
map <- fortify(map, region = 'ccaa')



for(i in 1:length(unique_dates)){
  this_date <- unique_dates[i]
    today_map <- map
    today_centroids <- centroids
    today_pd <- pd

  today_pd <- today_pd %>%
      mutate(ccaa = cat_transform(ccaa)) %>%
    group_by(ccaa) %>%
    # filter(date == max(date)) %>%
    filter(date == this_date) %>%
    ungroup %>%
    dplyr::select(date, ccaa, deaths, deaths_non_cum,
                  cases, cases_non_cum) %>%
    left_join(popper) %>%
    mutate(cases_per_million = cases / pop * 1000000,
           deaths_per_million = deaths / pop * 1000000) %>%
    mutate(cases_per_million_non_cum = cases_non_cum / pop * 1000000,
           deaths_per_million_non_cum = deaths_non_cum / pop * 1000000)
  
  today_centroids <- left_join(today_centroids, today_pd)

  
  # join data
  today_map$ccaa <- today_map$id
  today_map <- left_join(today_map, today_pd)
  var <- 'deaths_per_million'
  today_map$var <- as.numeric(unlist(today_map[,var]))
  today_map$var <- ifelse(is.na(today_map$var), 0, today_map$var)
  today_centroids <- today_centroids[,c('ccaa', 'x', 'y', var)]
  today_centroids <- today_centroids %>%
    filter(ccaa %in% today_map$ccaa)
  today_centroids$var <- today_centroids[,var]
  today_centroids$var <- ifelse(is.na(today_centroids$var), 0, today_centroids$var)

  
  # cols <- rev(RColorBrewer::brewer.pal(n = 9, name = 'Spectral'))
  # cols <- c('#A16928','#bd925a','#d6bd8d','#edeac2','#b5c8b8','#79a7ac','#2887a1')
  # cols <- c('#009392','#39b185','#9ccb86','#e9e29c','#eeb479','#e88471','#cf597e')
  # cols <- c('#008080','#70a494','#b4c8a8','#f6edbd','#edbb8a','#de8a5a','#ca562c')
  cols <- rev(colorRampPalette(c('darkred', 'red', 'darkorange', 'orange', 'yellow', 'white'))(17))
  g <- ggplot(data = today_map,
           aes(x = long,
               y = lat,
               group = group)) +
      geom_polygon(aes(fill = var),
                   lwd = 0.3,
                   # color = 'darkgrey',
                   color = NA,
                   size = 0.6) +
        scale_fill_gradientn(name = '',
                             colours = cols,
                             breaks = seq(0, 1100, 50),
                             limits = c(0, 1100)) +
    # scale_fill_() +
    ggthemes::theme_map() +
    theme(legend.position = 'right',
          plot.title = element_text(size = 24)) +
    guides(fill = guide_colorbar(direction= 'vertical',
                                 barwidth = 1,
                                 barheight = 30,
                                 label.position = 'left')) +
    labs(subtitle = 'Cumulative COVID-19 deaths per million population',
         title = paste0(format(this_date, '%B %d, %Y'))) +
    geom_text(data = today_centroids,
              aes(x = x,
                  y = y,
                  group = NA,
                  label = paste0(ccaa, '\n',
                                 round(var, digits = 2))),
              alpha = 0.8,
              size = 1.5)
  
  ggsave(paste0('/tmp/animation_map/', this_date, '.png'),
         plot = g,
         height = 6, width = 9)
}

# Command line
cd /tmp/animation_map
mogrify -resize 50% *.png
convert -delay 20 -loop 0 *.png result.gif

Deaths overall over time Spain

df_country %>% filter(country == 'Spain') %>% arrange(date) %>% tail

# A tibble: 6 x 10
# Groups:   country [1]
  country date        cases deaths   uci hospitalizations cases_non_cum
  <chr>   <date>      <dbl>  <dbl> <dbl>            <int>         <dbl>
1 Spain   2020-04-15 182816  19130  7916                0          5183
2 Spain   2020-04-16 188068  19478  7338                0          5252
3 Spain   2020-04-17 192920  20043  7553                0          4852
4 Spain   2020-04-18 195470  20453  7668                0          2550
5 Spain   2020-04-19 201517  20852  7698                0          6047
6 Spain   2020-04-20 205602  21282  7705                0          4085
# … with 3 more variables: deaths_non_cum <dbl>, uci_non_cum <dbl>, iso <chr>

Deaths yesterday

pd <- df_country
pd$value <- pd$deaths_non_cum
the_date <- Sys.Date() - 1
pd <- pd %>%
  filter(date == the_date) %>%
  dplyr::select(country, iso, cases, cases_non_cum,
                deaths, value) %>%
  dplyr::arrange(desc(value)) %>%
  left_join(world_pop %>% dplyr::select(-country)) %>%
  mutate(value_per_million = value / pop * 1000000) #%>% 
  # arrange(desc(value_per_million))
pd <- pd[1:10,]
pd$country <- factor(pd$country, levels = pd$country)
ggplot(data = pd,
       aes(x = country,
           y = value)) +
  geom_bar(stat = 'identity',
           fill = 'black') +
  theme_simple() +
  geom_text(aes(label = value),
            nudge_y = -20,
            size = 4,
            color = 'white') +
  labs(title = paste0('Confirmed COVID-19 deaths on ', the_date),
       x = '', y = '')

pd

# A tibble: 10 x 10
# Groups:   country [10]
   country iso    cases cases_non_cum deaths value    pop region sub_region
   <fct>   <chr>  <dbl>         <dbl>  <dbl> <dbl>  <dbl> <chr>  <chr>     
 1 US      USA   784326         25240  42094  1433 3.27e8 Ameri… Northern …
 2 France  FRA   156480          2383  20292   548 6.70e7 Europe Western E…
 3 United… GBR   125856          4684  16550   455 6.65e7 Europe Northern …
 4 Italy   ITA   181228          2256  24114   454 6.04e7 Europe Southern …
 5 Spain   ESP   205602          4085  21282   430 4.67e7 Europe Southern …
 6 Germany DEU   147065          1881   4862   276 8.29e7 Europe Western E…
 7 Canada  CAN    37658          2025   1726   162 3.71e7 Ameri… Northern …
 8 Belgium BEL    39983          1487   5828   145 1.14e7 Europe Western E…
 9 Brazil  BRA    40743          2089   2587   125 2.09e8 Ameri… Latin Ame…
10 Turkey  TUR    90980          4674   2140   123 8.23e7 Asia   Western A…
# … with 1 more variable: value_per_million <dbl>

Deaths per million yesterday per CCAA

pd <- esp_df
pd$value <- pd$deaths_non_cum
the_date <- max(pd$date)
pd <- pd %>%
  filter(date == max(date)) %>%
  dplyr::select(ccaa, cases, cases_non_cum,
                deaths, value) %>%
  dplyr::arrange(desc(value)) %>%
  left_join(esp_pop)%>%
  mutate(value_per_million = value / pop * 1000000) #%>% 
  # arrange(desc(value_per_million))
pd <- pd[1:10,]
pd$ccaa <- factor(pd$ccaa, levels = pd$ccaa)
ggplot(data = pd,
       aes(x = ccaa,
           y = value)) +
  geom_bar(stat = 'identity',
           fill = 'black') +
  theme_simple() +
  geom_text(aes(label = value),
            nudge_y = -20,
            size = 4,
            color = 'white')

pd

# A tibble: 10 x 7
   ccaa          cases cases_non_cum deaths value     pop value_per_million
   <fct>         <dbl>         <dbl>  <dbl> <dbl>   <dbl>             <dbl>
 1 Cataluña      43112          1436   4152   143 7675217             18.6 
 2 Madrid        57997          1034   7460   109 6663394             16.4 
 3 CLM           17045           249   2075    54 2032863             26.6 
 4 CyL           16259           402   1521    28 2399548             11.7 
 5 País Vasco    12810           182   1103    22 2207776              9.96
 6 Navarra        4831            96    397    12  654214             18.3 
 7 C. Valenciana 10399            60   1089    10 5003769              2.00
 8 La Rioja       4483           112    294     9  316798             28.4 
 9 Extremadura    3269            26    397     8 1067710              7.49
10 Galicia        8468           169    360     8 2699499              2.96

Deaths yesterday animation

dir.create('/tmp/animation_deaths')
dates <- seq(as.Date('2020-03-17'), (Sys.Date()-1), by = 1)
for(i in 1:length(dates)){
  this_date <- dates[i]
  pd <- df_country
  pd$value <- pd$deaths_non_cum
  pd <- pd %>%
    filter(date == max(this_date)) %>%
    dplyr::select(country, cases, cases_non_cum,
                  deaths, value) %>%
    dplyr::arrange(desc(value))
  pd <- pd[1:10,]
  pd <- pd %>% filter(value > 0)
  pd$country <- gsub(' ', '\n', pd$country)
  pd$country <- factor(pd$country, levels = pd$country)
  pd$color_var <- pd$country == 'Spain'
  if('Spain' %in% pd$country){
    cols <- rev(c('darkred', 'black'))
  } else {
    cols <- 'black'
  }
  g <- ggplot(data = pd,
         aes(x = country,
             y = value)) +
    geom_bar(stat = 'identity',
             aes(fill = color_var),
             alpha = 0.8,
             show.legend = FALSE) +
    theme_simple() +
    geom_text(aes(label = value),
              nudge_y = max(pd$value) * .05,
              size = 5,
              color = 'black') +
    labs(title = 'Daily (non-cumulative) COVID-19 deaths',
         subtitle = format(this_date, '%B %d')) +
    labs(x = 'Country',
         y = 'Deaths') +
    theme(axis.text = element_text(size = 15),
          plot.subtitle = element_text(size = 20)) +
    scale_fill_manual(name ='',
                      values = cols) +
    ylim(0, 900)
  ggsave(filename = paste0('/tmp/animation_deaths/', this_date, '.png'),
         g)
}
# Command line

cd /tmp/animation_deaths
mogrify -resize 50% *.png
convert -delay 50 -loop 0 *.png result.gif

Heatmap

pd <- by_country <-  esp_df %>% mutate(country = 'Spain') %>% 
  bind_rows(ita %>% mutate(country = 'Italy')) %>%
  bind_rows(por_df %>% mutate(country = 'Portugal')) %>%
  bind_rows(fra_df %>% mutate(country = 'France'))
pd$value <- pd$deaths_non_cum
max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% ungroup %>% summarise(d = min(d)) %>% .$d
# pd$value <- ifelse(is.na(pd$value), 0, pd$value)
left <- expand.grid(date = seq(min(pd$date),
                               max(pd$date),
                               by = 1),
                    ccaa = sort(unique(pd$ccaa)))
right <- pd %>% dplyr::select(date, ccaa, value)
pd <- left_join(left, right) %>% mutate(value = ifelse(is.na(value), NA, value))
pd <- left_join(pd, by_country %>% dplyr::distinct(country, ccaa)) %>%
  filter(date <= max_date) %>%
  filter(value > 0)
the_limits <- c(1, 600)
the_breaks <- c(1, seq(100, 600, length = 6)) #seq(0, 600, length = 7)
pd$ccaa <- factor(pd$ccaa, levels = rev(unique(sort(pd$ccaa))))
ggplot(data = pd,
       aes(x = date,
           y = ccaa,
           color = value,
           size = value)) +
  # geom_tile(color = 'white') +
  geom_point(alpha = 0.8) +
  # geom_tile() +
  scale_color_gradientn(colors = rev(colorRampPalette(brewer.pal(n = 8, 'Spectral'))(5)),
                        name = '',
                        limits = the_limits,
                        breaks = the_breaks) +
    # scale_fill_gradientn(colors = rev(colorRampPalette(brewer.pal(n = 8, 'Spectral'))(5)),
    #                     name = '',
    #                     limits = the_limits,
    #                     breaks = the_breaks) +
  scale_size_area(name = '', limits = the_limits, breaks = the_breaks, max_size = 10) +
  
  theme_simple() +
  facet_wrap(~country, scales = 'free_y') +
  theme(strip.text = element_text(size = 20),
        axis.title = element_blank(),
        axis.text = element_text(size = 10),
        axis.text.x = element_text(size = 12)) +
  guides(color = guide_legend(), size = guide_legend()) +
  labs(title = 'Daily (non-cumulative) COVID-19 deaths by sub-state regions',
       caption = paste0('Data as of ', max_date))

ggsave('/tmp/1.png',
       width = 10,
       height = 8)

Heatmap per population

pd <- by_country <-  esp_df %>% mutate(country = 'Spain') %>%  bind_rows(ita %>% mutate(country = 'Italy'))
poppy <- bind_rows(ita_pop, esp_pop)
pd <- pd %>% left_join(poppy)
pd$value <- pd$deaths_non_cum / pd$pop * 1000000
max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% ungroup %>% summarise(d = min(d)) %>% .$d
# pd$value <- ifelse(is.na(pd$value), 0, pd$value)
left <- expand.grid(date = seq(min(pd$date),
                               max(pd$date),
                               by = 1),
                    ccaa = sort(unique(pd$ccaa)))
right <- pd %>% dplyr::select(date, ccaa, value)
pd <- left_join(left, right) %>% mutate(value = ifelse(is.na(value), NA, value))
pd <- left_join(pd, by_country %>% dplyr::distinct(country, ccaa)) %>%
  filter(date <= max_date) %>%
  filter(value > 0)
the_limits <- c(1, 60)
the_breaks <- c(1, seq(10, 60, length = 6)) #seq(0, 600, length = 7)
pd$ccaa <- factor(pd$ccaa, levels = rev(unique(sort(pd$ccaa))))
ggplot(data = pd,
       aes(x = date,
           y = ccaa,
           color = value,
           size = value)) +
  # geom_tile(color = 'white') +
  geom_point(alpha = 0.8) +
  scale_color_gradientn(colors = rev(colorRampPalette(brewer.pal(n = 8, 'Spectral'))(5)),
                        name = '',
                        limits = the_limits,
                        breaks = the_breaks) +
  scale_size_area(name = '', limits = the_limits, breaks = the_breaks, max_size = 10) +
  theme_simple() +
  facet_wrap(~country, scales = 'free') +
  theme(strip.text = element_text(size = 26),
        axis.title = element_blank(),
        axis.text = element_text(size = 16),
        axis.text.x = element_text(size = 12)) +
  guides(color = guide_legend(), size = guide_legend()) +
  labs(title = 'Daily COVID-19 deaths per 1,000,000 population by sub-state regions',
       caption = paste0('Data as of ', max_date))

ggsave('/tmp/2.png',
       width = 10,
       height = 8)

Madrid vs rest of state

place_transform <- function(x){ifelse(x == 'Madrid', 'Madrid',
                                      # ifelse(x == 'Cataluña', 'Cataluña',
                                             'Otras CCAA')
  # )
}
place_transform_ita <- function(x){
  ifelse(x == 'Lombardia', 'Lombardia', 
         # ifelse(x == 'Emilia Romagna', 'Emilia Romagna', 
                'Otras regiones italianas')
  # )
}
pd <- esp_df %>% mutate(country = 'España') %>%
  mutate(ccaa = place_transform(ccaa)) %>%
  bind_rows(ita %>% mutate(ccaa = place_transform_ita(ccaa),
                           country = 'Italia')) %>%
  group_by(country, ccaa, date) %>% 
  summarise(cases = sum(cases),
            uci = sum(uci),
            deaths = sum(deaths),
            cases_non_cum = sum(cases_non_cum),
            deaths_non_cum = sum(deaths_non_cum),
            uci_non_cum = sum(uci_non_cum)) %>%
  left_join(esp_pop %>%
              mutate(ccaa = place_transform(ccaa)) %>%
              bind_rows(ita_pop %>% mutate(ccaa = place_transform_ita(ccaa))) %>%
              group_by(ccaa) %>%
              summarise(pop = sum(pop))) %>%
  mutate(deaths_non_cum_p = deaths_non_cum / pop * 1000000) %>%
  group_by(country, date) %>%
  mutate(p_deaths_non_cum_country = deaths_non_cum / sum(deaths_non_cum) * 100,
         p_deaths_country = deaths / sum(deaths) * 100)
pd$ccaa <- factor(pd$ccaa,
                  levels = c('Madrid',
                             # 'Cataluña',
                             'Otras CCAA',
                             'Lombardia',
                             # 'Emilia Romagna',
                             'Otras regiones italianas'))
cols <- c(
  rev(brewer.pal(n = 3, 'Reds'))[1:2],
  rev(brewer.pal(n = 3, 'Blues'))[1:2]
)
ggplot(data = pd,
       aes(x = date,
           y = deaths_non_cum_p,
           fill = ccaa,
           group = ccaa)) +
  geom_bar(stat = 'identity',
           position = position_dodge(width = 0.99)) +
  scale_fill_manual(name = '', values = cols) +
  scale_color_manual(name = '', values = cols) +
  # geom_line(size = 0.2,
  #           aes(color = ccaa)) +
  xlim(as.Date('2020-03-09'),
       Sys.Date()-1) +
  theme_simple() +
  labs(x = 'Fecha',
       y = 'Muertes diarias por 1.000.000',
       title = 'Muertes por 1.000.000 habitantes') +
  theme(legend.position = 'top') +
  geom_text(aes(label = round(deaths_non_cum_p, digits = 1),
                color = ccaa,
                y = deaths_non_cum_p + 2,
                group = ccaa),
            size = 2.5,
            angle = 90,
            position = position_dodge(width = 0.99))

label_data <- pd %>%
  filter(country  %in% c('Italia', 'España')) %>%
  group_by(country) %>%
  filter(date == max(date))  %>%
  mutate(label = gsub('\nitalianas', '',  gsub(' ', '\n', ccaa))) %>%
  mutate(x = date - 2,
         y = p_deaths_country + 
           ifelse(p_deaths_country > 50, 10, -10))
ggplot(data = pd %>% group_by(country) %>% mutate(start_day = dplyr::first(date[deaths >=50])) %>% filter(date >= start_day),
       aes(x = date,
           y = p_deaths_country,
           color = ccaa,
           group = ccaa)) +
  # geom_bar(stat = 'identity',
  #          position = position_dodge(width = 0.99)) +
  scale_fill_manual(name = '', values = cols) +
  scale_color_manual(name = '', values = cols) +
  geom_line(size = 2,
            aes(color = ccaa)) +
  geom_point(size = 3,
             aes(color = ccaa)) +
  facet_wrap(~country, scales = 'free_x') +
  # xlim(as.Date('2020-03-09'),
  #      Sys.Date()-1) +
  theme_simple() +
  geom_hline(yintercept = 50, lty = 2, alpha = 0.6) +
  # geom_line(lty = 2, alpha = 0.6) +
  labs(x = 'Fecha',
       y = 'Porcentaje de muertes',
       title = 'Porcentaje de muertes del país: región más afectada vs. resto del país',
       subtitle = 'A partir del primer día en cada país con 50 o más muertes') +
  theme(legend.position = 'top',
        strip.text = element_text(size = 30),
        legend.text = element_text(size = 10))  +
  guides(color = guide_legend(nrow = 2,
                              keywidth = 5)) +
  geom_text(data = label_data,
            aes(x = x - 2,
                y = y,
                label = label,
                color = ccaa),
            size = 6,
            show.legend = FALSE)

Italy regions, Spanish regions, Chinese regions (adjusted for population)

# Spanish data
a <- esp_df %>%
  left_join(esp_pop) %>%
  mutate(country = 'Spain')
# Italian data
b <- ita %>%
  left_join(ita_pop) %>%
  mutate(country = 'Italy')
# Chinese data
d <- df %>% filter(country == 'China') %>%
  mutate(cases = cases) %>%
  mutate(ccaa = district) %>%
  mutate(country = 'China') %>%
  left_join(chi_pop)
# join
joined <- bind_rows(a, b, d)
# Get deaths per milllion
joined$deaths_pm <- joined$deaths / joined$pop * 1000000
joined$cases_pm <- joined$cases / joined$pop * 1000000

# Get the days since paradigm
x_deaths <- 5
x_deaths_pm <- 5
x_cases <- 50
x_cases_pm <- 50
joined <- joined %>%
  arrange(date) %>%
  group_by(ccaa) %>%
  mutate(start_deaths = min(date[deaths >= x_deaths]),
         start_cases = min(date[cases >= x_cases]),
         start_deaths_pm = min(date[deaths_pm >= x_deaths_pm]),
         start_cases_pm = min(date[cases_pm >= x_cases_pm])) %>%
  ungroup %>%
  mutate(days_since_start_deaths = date - start_deaths,
         days_since_start_cases = date - start_cases,
         days_since_start_deaths_pm = date - start_deaths_pm,
         days_since_start_cases_pm = date - start_cases_pm) 

# Define plot data
pd <- joined %>% filter(days_since_start_deaths_pm >= 0) %>%
  mutate(country = ifelse(country == 'China',
                          'Hubei (China)',
                          ifelse(country == 'Italy', 'Italia', 'España')))

lombardy_location <- (max(pd_big$days_since_start_deaths_pm[pd_big$ccaa == 'Lombardia']))

Error in eval(expr, envir, enclos): object 'pd_big' not found

# Define label data
label_data <- pd %>% group_by(ccaa) %>% filter(
                                                          (
                                                            (country == 'Hubei (China)' & days_since_start_deaths_pm == 22) |
                                                            (date == max(date) & country == 'España' & deaths_pm > 40 & days_since_start_deaths_pm >= 7) & ccaa != 'CyL' |
                                                              (date == max(date) & country == 'Italia' &
                                                                 ccaa != 'Liguria' & days_since_start_deaths_pm > 15)
                                                          ))
# Get differential label data based on what to be emphasized
bigs <- c('Madrid', 'Lombardia', 'Hubei')
label_data_big <- label_data %>%
  filter(ccaa %in% bigs)
label_data_small <- label_data %>%
  filter(!ccaa %in% bigs)

pd_big <- pd %>%
  filter(ccaa %in% bigs)
pd_small <- pd %>%
  filter(!ccaa %in% bigs)

# cols <- colorRampPalette(RColorBrewer::brewer.pal(n = 8, name = 'Set2'))(length(unique(pd$country)))
# cols <- rainbow(3)
cols <- c('darkred', '#FF6633', '#006666')

ggplot(data = pd_big,
       aes(x = days_since_start_deaths_pm,
           y = deaths_pm,
           group = ccaa)) +
  geom_line(aes(color = country),
            alpha = 0.9,
            size = 2) +
  geom_line(data = pd_small,
            aes(x = days_since_start_deaths_pm,
                y = deaths_pm,
                color = country),
            alpha = 0.7,
            size = 1) +
  scale_y_log10() +
  scale_color_manual(name = '',
                     values = c(cols)) +
  theme_simple() +
  theme(legend.position = 'top') +
  labs(x = 'Dias desde "el comienzo del brote"',
       y = 'Muertes por millón de habitantes',
       title = 'Muertes por 1.000.000 habitantes',
       subtitle = paste0('Dia 0 ("comienzo del brote") = primer día a ', x_deaths_pm, ' o más muertes acumuladas por milión de población\nLíneas rojas: CCAA; líneas verde-azules: regiones italianas; línea naranja: Hubei, China'),
       caption = '@joethebrew | www.databrew.cc') +
  geom_text(data = label_data_big,
            aes(x = days_since_start_deaths_pm + 0.6,
                y = (deaths_pm + 50),
                label = gsub(' ', '\n', ccaa),
                color = country),
            size = 8,
            alpha = 0.9,
            show.legend = FALSE) +
    geom_text(data = label_data_small,
            aes(x = days_since_start_deaths_pm + 0.6,
                y = deaths_pm  + (log(deaths_pm)/2),
                label = gsub(' ', '\n', ccaa),
                color = country),
            size = 5,
            alpha = 0.6,
            show.legend = FALSE) +
  theme(axis.text = element_text(size = 14),
        axis.title = element_text(size = 16),
        legend.text = element_text(size = 16),
        plot.title = element_text(size = 25))  +
  xlim(0, lombardy_location + 5)

Error in limits(c(...), "x"): object 'lombardy_location' not found

Italy regions, Spanish regions, Chinese regions (raw numbers, not adjusted for population)

# Spanish data
a <- esp_df %>%
  left_join(esp_pop) %>%
  mutate(country = 'Spain')
# Italian data
b <- ita %>%
  left_join(ita_pop) %>%
  mutate(country = 'Italy')
# Chinese data
d <- df %>% filter(country == 'China') %>%
  mutate(cases = cases) %>%
  mutate(ccaa = district) %>%
  mutate(country = 'China') %>%
  left_join(chi_pop)
# join
joined <- bind_rows(a, b, d)
# Get deaths per milllion
joined$deaths_pm <- joined$deaths / joined$pop * 1000000
joined$cases_pm <- joined$cases / joined$pop * 1000000

# Get the days since paradigm
x_deaths <- 5
x_deaths_pm <- 5
x_cases <- 50
x_cases_pm <- 50
joined <- joined %>%
  arrange(date) %>%
  group_by(ccaa) %>%
  mutate(start_deaths = min(date[deaths >= x_deaths]),
         start_cases = min(date[cases >= x_cases]),
         start_deaths_pm = min(date[deaths_pm >= x_deaths_pm]),
         start_cases_pm = min(date[cases_pm >= x_cases_pm])) %>%
  ungroup %>%
  mutate(days_since_start_deaths = date - start_deaths,
         days_since_start_cases = date - start_cases,
         days_since_start_deaths_pm = date - start_deaths_pm,
         days_since_start_cases_pm = date - start_cases_pm) 

# Define plot data
pd <- joined %>% filter(days_since_start_deaths >= 0) %>%
  mutate(country = ifelse(country == 'China',
                          'China',
                          ifelse(country == 'Italy', 'Italia', 'España')))

# Define label data
label_data <- pd %>% group_by(ccaa) %>% filter(
                                                          (
                                                            (country == 'China' & deaths >10 & days_since_start_deaths == 29) |
                                                            (date == max(date) & country == 'España' & deaths > 90) |
                                                              (date == max(date) & country == 'Italia' &
                                                                 ccaa != 'Liguria' & days_since_start_deaths > 10)
                                                          ))
# Get differential label data based on what to be emphasized
label_data_big <- label_data %>%
  filter(ccaa %in% c('Madrid', 'Lombardia', 'Hubei'))
label_data_small <- label_data %>%
  filter(!ccaa %in% c('Madrid', 'Lombardia', 'Hubei'))

pd_big <- pd %>%
  filter(ccaa %in% c('Madrid', 'Lombardia', 'Hubei'))
pd_small <- pd %>%
  filter(!ccaa %in% c('Madrid', 'Lombardia', 'Hubei'))

# cols <- colorRampPalette(RColorBrewer::brewer.pal(n = 8, name = 'Set2'))(length(unique(pd$country)))
# cols <- rainbow(3)
cols <- c( '#FF6633',  'darkred', '#006666')
ggplot(data = pd_big,
       aes(x = days_since_start_deaths,
           y = deaths,
           group = ccaa)) +
  geom_line(aes(color = country),
            alpha = 0.9,
            size = 2) +
  geom_line(data = pd_small,
            aes(x = days_since_start_deaths,
                y = deaths,
                color = country),
            alpha = 0.7,
            size = 1) +
  scale_y_log10() +
  scale_color_manual(name = '',
                     values = c(cols)) +
  theme_simple() +
  theme(legend.position = 'top') +
  labs(x = 'Dias desde el primer día con 5 o más muertes acumuladas',
       y = 'Muertes',
       title = 'Muertes por COVID-19',
       caption = '@joethebrew | www.databrew.cc') +
  geom_text(data = label_data_big,
            aes(x = days_since_start_deaths + 1.6,
                y = ifelse(ccaa == 'Hubei', (deaths -500),
                           ifelse(ccaa == 'Lombardia',  (deaths + 700),
                                   (deaths + 300))),
                label = gsub(' ', '\n', ccaa),
                color = country),
            size = 8,
            alpha = 0.9,
            show.legend = FALSE) +
    geom_text(data = label_data_small,
            aes(x = days_since_start_deaths + 1.6,
                align = 'left',
                y = deaths ,
                label = ccaa,
                # label = gsub(' ', '\n', ccaa),
                color = country),
            size = 5,
            alpha = 0.6,
            show.legend = FALSE) +
  theme(axis.text = element_text(size = 14),
        axis.title = element_text(size = 20),
        legend.text = element_text(size = 16),
        plot.title = element_text(size = 30))  +
  xlim(0, 50)

ANIMATION: Italy regions, Spanish regions, Chinese regions (raw numbers, not adjusted for population)

# Spanish data
a <- esp_df %>%
  left_join(esp_pop) %>%
  mutate(country = 'Spain')
# Italian data
b <- ita %>%
  left_join(ita_pop) %>%
  mutate(country = 'Italy')
# Chinese data
d <- df %>% filter(country == 'China') %>%
  mutate(cases = cases) %>%
  mutate(ccaa = district) %>%
  mutate(country = 'China') %>%
  left_join(chi_pop)
# join
joined <- bind_rows(a, b, d)
# Get deaths per milllion
joined$deaths_pm <- joined$deaths / joined$pop * 1000000
joined$cases_pm <- joined$cases / joined$pop * 1000000

# Get the days since paradigm
x_deaths <- 5
x_deaths_pm <- 5
x_cases <- 50
x_cases_pm <- 50
joined <- joined %>%
  arrange(date) %>%
  group_by(ccaa) %>%
  mutate(start_deaths = min(date[deaths >= x_deaths]),
         start_cases = min(date[cases >= x_cases]),
         start_deaths_pm = min(date[deaths_pm >= x_deaths_pm]),
         start_cases_pm = min(date[cases_pm >= x_cases_pm])) %>%
  ungroup %>%
  mutate(days_since_start_deaths = date - start_deaths,
         days_since_start_cases = date - start_cases,
         days_since_start_deaths_pm = date - start_deaths_pm,
         days_since_start_cases_pm = date - start_cases_pm) 

# Define plot data
pd <- joined %>% filter(days_since_start_deaths >= 0) %>%
  mutate(country = ifelse(country == 'China',
                          'China',
                          ifelse(country == 'Italy', 'Italia', 'España')))


add_zero <- function(x, n){
  x <- as.character(x)
  adders <- n - nchar(x)
  adders <- ifelse(adders < 0, 0, adders)
  for (i in 1:length(x)){
    if(!is.na(x[i])){
      x[i] <- paste0(
        paste0(rep('0', adders[i]), collapse = ''),
        x[i],
        collapse = '')  
    } 
  }
  return(x)
}
# # Define label data
# label_data <- pd %>% group_by(ccaa) %>% filter(
#                                                           (
#                                                             (country == 'China' & deaths >10 & days_since_start_deaths == 29) |
#                                                             (date == max(date) & country == 'España' & deaths > 90) |
#                                                               (date == max(date) & country == 'Italia' &
#                                                                  ccaa != 'Liguria' & days_since_start_deaths > 10)
#                                                           ))
# # Get differential label data based on what to be emphasized
# label_data_big <- label_data %>%
#   filter(ccaa %in% c('Madrid', 'Lombardia', 'Hubei'))
# label_data_small <- label_data %>%
#   filter(!ccaa %in% c('Madrid', 'Lombardia', 'Hubei'))
# 
pd_big <- pd %>%
  filter(ccaa %in% c('Madrid', 'Lombardia', 'Hubei'))
pd_small <- pd %>%
  filter(!ccaa %in% c('Madrid', 'Lombardia', 'Hubei'))



# cols <- colorRampPalette(RColorBrewer::brewer.pal(n = 8, name = 'Set2'))(length(unique(pd$country)))
# cols <- rainbow(3)
cols <- c( '#FF6633',  'darkred', '#006666')

the_dir <- '/tmp/animation/'
dir.create(the_dir)
the_dates <- sort(unique(c(pd_big$date, pd_small$date)))
for(i in 1:length(the_dates)){
  
  the_date <- the_dates[i]
  pd_big_sub <- pd_big %>% filter(date <= the_date)
  pd_big_current <- pd_big_sub %>% filter(date == the_date)
  pd_small_sub <- pd_small %>% filter(date <= the_date)
  pd_small_current <- pd_small_sub %>% filter(date == the_date)

  label_data_big <-
    pd_big_sub %>%
    filter(ccaa %in% c('Lombardia', 'Madrid', 'Hubei')) %>%
    group_by(ccaa) %>%
    filter(date == max(date)) %>%
    ungroup %>%
    mutate(days_since_start_deaths = ifelse(ccaa == 'Hubei' &
                                              days_since_start_deaths >32,
                                            32,
                                            days_since_start_deaths))
  
  label_data_small <-
    pd_small_sub %>%
    filter(ccaa %in% c('Emilia Romagna',
                       'Cataluña',
                       'CLM',
                       'País Vasco',
                       'Veneto',
                       'Piemonte',
                       'Henan',
                       'Heilongjiang')) %>%
    group_by(ccaa) %>%
    filter(date == max(date))

  n_countries <- length(unique(pd_big_sub$country))
  if(n_countries == 3){
    sub_cols  <- cols
  }
  if(n_countries == 2){
    sub_cols <- cols[c(1,3)]
  }
   if(n_countries == 1){
    sub_cols <- cols[1]
  }
  g <- ggplot(data = pd_big_sub,
       aes(x = days_since_start_deaths,
           y = deaths,
           group = ccaa)) +
  geom_line(aes(color = country),
            alpha = 0.9,
            size = 2) +
  geom_line(data = pd_small_sub,
            aes(x = days_since_start_deaths,
                y = deaths,
                color = country),
            alpha = 0.7,
            size = 1) +
    geom_point(data = pd_big_current,
               aes(x = days_since_start_deaths,
                y = deaths,
                color = country),
               size = 3) +
    geom_point(data = pd_small_current,
               aes(x = days_since_start_deaths,
                y = deaths,
                color = country),
               size = 1, alpha = 0.6) +
    scale_y_log10(limits = c(5, 4500)) +
  scale_color_manual(name = '',
                     values = sub_cols) +
  theme_simple() +
  theme(legend.position = 'top') +
  labs(x = 'Dias desde el primer día con 5 o más muertes acumuladas',
       y = 'Muertes',
       title = format(the_date, '%d %b'),
       subtitle = 'Muertes por COVID-19',
       caption = '@joethebrew | www.databrew.cc') +
  geom_text(data = label_data_big,
            aes(x = days_since_start_deaths + 1,
                y = deaths,
                hjust = 0,
                label = gsub(' ', '\n', ccaa),
                color = country),
            size = 8,
            alpha = 0.9,
            show.legend = FALSE) +
    geom_text(data = label_data_small,
            aes(x = days_since_start_deaths + 1.6,
                y = deaths ,
                label = ccaa,
                # label = gsub(' ', '\n', ccaa),
                color = country),
            size = 5,
            alpha = 0.6,
            show.legend = FALSE) +
  theme(axis.text = element_text(size = 14),
        axis.title = element_text(size = 20),
        legend.text = element_text(size = 16),
        plot.title = element_text(size = 35),
        plot.subtitle = element_text(size = 24))  +
  xlim(0, 38) 
  message(i)
  ggsave(paste0(the_dir, add_zero(i, 3), '.png'),
         height = 7,
         width = 10.5)
}

# Command line
cd /tmp/animation
mogrify -resize 50% *.png
convert -delay 20 -loop 0 *.png result.gif

ANIMATION: Spain only

# Spanish data
a <- esp_df %>%
  left_join(esp_pop) %>%
  mutate(country = 'Spain')
joined <- a
# Get deaths per milllion
joined$deaths_pm <- joined$deaths / joined$pop * 1000000
joined$cases_pm <- joined$cases / joined$pop * 1000000

# Get the days since paradigm
x_deaths <- 5
x_deaths_pm <- 5
x_cases <- 50
x_cases_pm <- 50
joined <- joined %>%
  arrange(date) %>%
  group_by(ccaa) %>%
  mutate(start_deaths = min(date[deaths >= x_deaths]),
         start_cases = min(date[cases >= x_cases]),
         start_deaths_pm = min(date[deaths_pm >= x_deaths_pm]),
         start_cases_pm = min(date[cases_pm >= x_cases_pm])) %>%
  ungroup %>%
  mutate(days_since_start_deaths = date - start_deaths,
         days_since_start_cases = date - start_cases,
         days_since_start_deaths_pm = date - start_deaths_pm,
         days_since_start_cases_pm = date - start_cases_pm) 

# Define plot data
pd <- joined %>% filter(days_since_start_deaths >= 0) %>%
  mutate(country = ifelse(country == 'China',
                          'China',
                          ifelse(country == 'Italy', 'Italia', 'España')))

bigs <- c('Madrid', 'Cataluña', 'CLM', 'CyL', 'País Vasco', 'La Rioja')
pd_big <- pd %>%
  filter(ccaa %in% bigs)
pd_small <- pd %>%
  filter(!ccaa %in% bigs)



# cols <- colorRampPalette(RColorBrewer::brewer.pal(n = 8, name = 'Set2'))(length(unique(pd$country)))
# cols <- rainbow(3)
cols <- colorRampPalette(c('#A16928','#bd925a','#d6bd8d','#edeac2', '#b5c8b8','#79a7ac','#2887a1'))(length(unique(pd$country)))

the_dir <- '/tmp/animation2/'
dir.create(the_dir)
the_dates <- sort(unique(c(pd_big$date, pd_small$date)))
for(i in 1:length(the_dates)){
  
  the_date <- the_dates[i]
  pd_big_sub <- pd_big %>% filter(date <= the_date)
  pd_big_current <- pd_big_sub %>% filter(date == the_date)
  pd_small_sub <- pd_small %>% filter(date <= the_date)
  pd_small_current <- pd_small_sub %>% filter(date == the_date)

  label_data_big <-
    pd_big_sub %>%
    filter(ccaa %in% bigs) %>%
    group_by(ccaa) %>%
    filter(date == max(date)) %>%
    ungroup
  
  label_data_small <-
    pd_small_sub %>%
    group_by(ccaa) %>%
    filter(date == max(date))
# sub_cols <- colorRampPalette(c('#A16928','#bd925a','#d6bd8d','#edeac2', '#b5c8b8','#79a7ac','#2887a1'))(length(unique(pd$ccaa)))
  sub_cols <- colorRampPalette(RColorBrewer::brewer.pal(n = 8, name = 'Dark2'))(length(unique(pd$ccaa)))
  # sub_cols <- rainbow((length(unique(pd$ccaa))))
  
  g <- ggplot(data = pd_big_sub,
       aes(x = days_since_start_deaths,
           y = deaths,
           group = ccaa)) +
  geom_line(aes(color = ccaa),
            alpha = 0.9,
            size = 2) +
  geom_line(data = pd_small_sub,
            aes(x = days_since_start_deaths,
                y = deaths,
                color = ccaa),
            alpha = 0.7,
            size = 1) +
    geom_point(data = pd_big_current,
               aes(x = days_since_start_deaths,
                y = deaths,
                color = ccaa),
               size = 3) +
    geom_point(data = pd_small_current,
               aes(x = days_since_start_deaths,
                y = deaths,
                color = ccaa),
               size = 1, alpha = 0.6) +
    geom_point(data = pd,
               aes(x = days_since_start_deaths,
                y = deaths,
                color = ccaa),
               size = 1, alpha = 0.01) +
    scale_y_log10(limits = c(5, max(pd$deaths)*1.2),
                  breaks = c(10, 50, 100, 500, 1000)) +
  scale_color_manual(name = '',
                     values = sub_cols) +
  theme_simple() +
  theme(legend.position = 'top') +
  labs(x = 'Dias desde el primer día con 5 o más muertes acumuladas',
       y = 'Muertes',
       title = format(the_date, '%d %b'),
       subtitle = 'Muertes por COVID-19',
       caption = '@joethebrew | www.databrew.cc')   +
  theme(axis.text = element_text(size = 14),
        axis.title = element_text(size = 20),
        legend.text = element_text(size = 16),
        plot.title = element_text(size = 35),
        plot.subtitle = element_text(size = 24))  +
  xlim(0, 20) +
    theme(legend.position = 'none')
  message(i)
  if(nrow(label_data_big) > 0){
    g <- g +
      geom_text(data = label_data_big,
            aes(x = days_since_start_deaths + 0.2,
                y = deaths,
                hjust = 0,
                label = gsub(' ', ' ', ccaa),
                color = ccaa),
            size = 8,
            alpha = 0.9,
            show.legend = FALSE) +
    geom_text(data = label_data_small,
            aes(x = days_since_start_deaths + 0.2,
                y = deaths ,
                label = ccaa,
                # label = gsub(' ', '\n', ccaa),
                color = ccaa),
            size = 5,
            alpha = 0.6,
            show.legend = FALSE)
  }
  
  ggsave(paste0(the_dir, add_zero(i, 3), '.png'),
         height = 7,
         width = 12)
}

# Command line
cd /tmp/animation
mogrify -resize 50% *.png
convert -delay 25 -loop 0 *.png result.gif

Italy regions for Spanish regions

# Spanish data
a <- esp_df %>%
  left_join(esp_pop) %>%
  mutate(country = 'Spain')
# Italian data
b <- ita %>%
  left_join(ita_pop) %>%
  mutate(country = 'Italy')
# join
joined <- bind_rows(a, b)
# Get deaths per milllion
joined$deaths_pm <- joined$deaths / joined$pop * 1000000
joined$cases_pm <- joined$cases / joined$pop * 1000000

# Get the days since paradigm
x_deaths <- 5
x_deaths_pm <- 5
x_cases <- 50
x_cases_pm <- 50
joined <- joined %>%
  arrange(date) %>%
  group_by(ccaa) %>%
  mutate(start_deaths = min(date[deaths >= x_deaths]),
         start_cases = min(date[cases >= x_cases]),
         start_deaths_pm = min(date[deaths_pm >= x_deaths_pm]),
         start_cases_pm = min(date[cases_pm >= x_cases_pm])) %>%
  ungroup %>%
  mutate(days_since_start_deaths = date - start_deaths,
         days_since_start_cases = date - start_cases,
         days_since_start_deaths_pm = date - start_deaths_pm,
         days_since_start_cases_pm = date - start_cases_pm) 

ggplot(data = joined %>% filter(days_since_start_deaths_pm >= 0),
       aes(x = days_since_start_deaths_pm,
           y = deaths_pm,
           group = ccaa)) +
  geom_line(aes(color = country),
            alpha = 0.8,
            size = 2) +
  scale_y_log10() +
  scale_color_manual(name = '',
                     values = c('darkorange', 'purple')) +
  theme_simple() +
  theme(legend.position = 'none') +
  labs(x = 'Days since "start out outbreak"',
       y = 'Deaths per million',
       title = 'Deaths per capita, Italian regions vs. Spanish autonomous communities',
       subtitle = paste0('Day 0 ("start of outbreak") = first day at ', x_deaths_pm, ' or greater cumulative deaths per million'),
       caption = '@joethebrew | www.databrew.cc') +
  geom_text(data = joined %>% group_by(ccaa) %>% filter(date == max(date) & 
                                                          (
                                                            (country == 'Spain' & deaths_pm > 25) |
                                                              (country == 'Italy' & days_since_start_deaths_pm > 10)
                                                          )),
            aes(x = days_since_start_deaths_pm + 0.6,
                y = deaths_pm,
                label = gsub(' ', '\n', ccaa),
                color = country),
            size = 6) +
  theme(axis.text = element_text(size = 14),
        axis.title = element_text(size = 20)) +
  xlim(0, 23)

ggsave('/tmp/italy_comparison.png',
       height = 6,
       width = 10)


# Separate for Catalonia
pd <- joined %>% filter(days_since_start_deaths_pm >= 0) %>%
         mutate(country = ifelse(ccaa == 'Cataluña',
                                 'Catalonia',
                                 country)) %>%
  mutate(ccaa = ifelse(ccaa == 'Cataluña', 'Catalunya', ccaa))
pdcat <- pd %>% filter(country == 'Catalonia')
label_data <- pd %>% group_by(ccaa) %>% filter(date == max(date) & 
                                                          (
                                                            (country == 'Catalonia') |
                                                            (country == 'Spain' & deaths_pm > 25) |
                                                              (country == 'Italy' & days_since_start_deaths_pm > 10)
                                                          ))
ggplot(data = pd,
       aes(x = days_since_start_deaths_pm,
           y = deaths_pm,
           group = ccaa)) +
  geom_line(aes(color = country),
            alpha = 0.3,
            size = 1.5) +
    geom_line(data = pdcat,
              aes(color = country),
            alpha = 0.8,
            size = 2) +
      geom_point(data = pdcat %>% filter(date == max(date)),
              aes(color = country),
            alpha = 0.8,
            size = 4) +
  scale_y_log10() +
  scale_color_manual(name = '',
                     values = c('darkred', 'darkorange', "purple")) +
  theme_simple() +
  theme(legend.position = 'none') +
  labs(x = 'Dies des del "començament del brot"',
       y = 'Morts per milió',
       title = 'Morts per càpita: Catalunya, comunitats autònomes, regions italianes',
       subtitle = paste0('Dia 0 ("començament del brot") = primer dia a ', x_deaths_pm, ' o més morts acumulades per milió de població'),
       caption = '@joethebrew | www.databrew.cc') +
  geom_text(data = label_data,
            aes(x = days_since_start_deaths_pm +0.2 ,
                y = deaths_pm +3,
                hjust = 0,
                label = gsub(' ', '\n', ccaa),
                color = country),
            size = 6,
            alpha = 0.7) +
  theme(axis.text = element_text(size = 14),
        axis.title = element_text(size = 20)) +
  xlim(0, 24)

ggsave('/tmp/cat_italy_comparison.png',
       height = 6,
       width = 10)


# Straightforward Lombardy, Madrid, Cat comparison
specials <- c('Lombardia', 'Madrid')
pd <- joined %>% filter(days_since_start_deaths_pm >= 0) %>%
         mutate(country = ifelse(ccaa == 'Cataluña',
                                 'Catalonia',
                                 country)) %>%
  mutate(ccaa = ifelse(ccaa == 'Cataluña', 'Catalunya', ccaa))
pdcat <- pd %>% filter(ccaa %in%  specials)
label_data <- pd %>% group_by(ccaa) %>% filter(date == max(date) & 
                                                          (
                                                            # (country == 'Catalonia') |
                                                            (country == 'Spain' & deaths_pm > 20) |
                                                              (country == 'Italy' & days_since_start_deaths_pm >= 10)
                                                          ))
ggplot(data = pd,
       aes(x = days_since_start_deaths_pm,
           y = deaths_pm,
           group = ccaa)) +
  geom_line(aes(color = country),
            alpha = 0.3,
            size = 1.5) +
    geom_line(data = pdcat,
              aes(color = country),
            alpha = 0.8,
            size = 2) +
  scale_y_log10() +
  scale_color_manual(name = '',
                     values = c('darkred', 'darkorange', "purple")) +
  theme_simple() +
  theme(legend.position = 'none') +
  labs(x = 'Dias desde "el comienzo del brote"',
       y = 'Muertes por millón de habitantes',
       title = 'Muertes acumuladas por 1.000.000 habitantes',
       subtitle = paste0('Dia 0 ("comienzo del brote") = primer día a ', x_deaths_pm, ' o más muertes acumuladas por milión de población'),
       caption = '@joethebrew | www.databrew.cc') +
  geom_text(data = label_data %>% filter(!ccaa %in% specials),
            aes(x = days_since_start_deaths_pm + 0.4,
                y = deaths_pm +3,
                label = gsub(' ', '\n', ccaa),
                color = country),
            size = 5,
            alpha = 0.5) +
    geom_text(data = label_data %>% filter(ccaa %in% specials),
            aes(x = days_since_start_deaths_pm ,
                y = deaths_pm +30,
                label = gsub(' ', '\n', ccaa),
                color = country),
            size = 8,
            alpha = 0.8) +
  theme(axis.text = element_text(size = 14),
        axis.title = element_text(size = 20)) +
  xlim(0, 23)

ggsave('/tmp/mad_lom_italy_comparison.png',
       height = 6,
       width = 10)

Loop for regions of the world

isos <- sort(unique(world_pop$sub_region))
isos <- c('Central Asia', 'Eastern Asia', 'Eastern Europe',
          'Latin America and the Caribbean',
          'Northern Africa', 'Northern America',
          'Nothern Europe',
          'South-eastern Asia',
          'Southern Asia', 'Southern Europe',
          'Sub-Saharan Africa', 'Western Asia', 'Western Europe')
dir.create('/tmp/world')
for(i in 1:length(isos)){
  this_iso <- isos[i]
  message(i, ' ', this_iso)
  countries <- world_pop %>% filter(sub_region == this_iso)
  pd <- df %>%
    filter(!country %in% c('Guyan
                           a', 'Bahamas', 'The Bahamas')) %>%
          group_by(country, iso, date) %>%
          summarise(cases = sum(cases, na.rm = TRUE)) %>%
    ungroup %>%
    group_by(country) %>%
         filter(length(which(cases > 0)) > 1) %>%
    ungroup %>%
         filter(iso %in% countries$iso)
  if(nrow(pd) > 0){
    cols <- colorRampPalette(brewer.pal(n = 8, 'Spectral'))(length(unique(pd$country)))
cols <- sample(cols, length(cols))
    # Plot
n_countries <- (length(unique(pd$country)))
ggplot(data = pd,
       aes(x = date,
           # color = country,
           # fill = country,
           y = cases)) +
  theme_simple() +
  # geom_point() +
  # geom_line() +
  geom_area(fill = 'darkred', alpha = 0.3, color = 'darkred') +
  # scale_color_manual(name = '',
  #                    values = cols) +
  # scale_fill_manual(name = '',
  #                   values = cols) +
  theme(legend.position = 'none',
        axis.text = element_text(size = 6),
        strip.text = element_text(size = ifelse(n_countries > 20, 6,
                                                ifelse(n_countries > 10, 10,
                                                       ifelse(n_countries > 5, 11, 12))) ),
        legend.text = element_text(size = 6)) +
  # scale_y_log10() +
  facet_wrap(~country,
             scales = 'free') +
  labs(x = '',
       y = 'Confirmed cases',
       title = paste0('Confirmed cases of COVID-19 in ', this_iso)) 
  ggsave(paste0('/tmp/world/', this_iso, '.png'),
         width = 12, 
         height = 7)
  }



}

Rolling average new events

plot_data <- df_country %>% filter(country %in% c('Spain', 'France', 'Italy', 'Germany', 'Belgium', 'Norway')) %>% mutate(geo = country)
roll_curve(plot_data, pop = T)

dir.create('/tmp/countries')
roll_curve_country <- function(the_country = 'Spain'){
  plot_data <- df_country %>% filter(country %in% the_country) %>% mutate(geo = country)
  g1 <- roll_curve(plot_data, pop = F)
  g2 <- roll_curve(plot_data, pop = T)
  g3 <- roll_curve(plot_data, pop = F, deaths = T)
  g4 <- roll_curve(plot_data, pop = T, deaths = T)
  ggsave(paste0('/tmp/countries/', the_country, '1.png'), g1)
  ggsave(paste0('/tmp/countries/', the_country, '2.png'), g2)
  ggsave(paste0('/tmp/countries/', the_country, '3.png'), g3)
  ggsave(paste0('/tmp/countries/', the_country, '4.png'), g4)
}


countries <- c('Spain', 'France', 'Italy', 'Germany', 'Belgium', 'Norway', 'US', 'United Kingdom', 'Korea, South',
  'China', 'Japan', 'Switzerland', 'Sweden', 'Denmark', 'Netherlands', 'Iran', 'Canada')
for(i in 1:length(countries)){
  roll_curve_country(the_country = countries[i])
}

Error in ts(x): 'ts' object must have one or more observations

# Cases
plot_data <- df_country %>% filter(country == 'Spain') %>% mutate(geo = country)
roll_curve(plot_data)

# Cases adjusted
plot_data <- df_country %>% filter(country == 'Spain') %>% mutate(geo = country)
roll_curve(plot_data, pop = T)

# Deaths
plot_data <- df_country %>% filter(country == 'Spain') %>% mutate(geo = country)
roll_curve(plot_data, deaths = T)

# Cases adjusted
plot_data <- df_country %>% filter(country == 'Spain') %>% mutate(geo = country)
roll_curve(plot_data, pop = T, deaths = T)

plot_data <- esp_df  %>% mutate(geo = ccaa)

roll_curve(plot_data, pop = T, deaths = T)

plot_data <- df_country %>% filter(country == 'Spain') %>% mutate(geo = country)

roll_curve(plot_data, deaths = T)

# Latest in Spain
pd <- esp_df %>%
  filter(date == max(date)) %>%
  mutate(p = deaths / sum(deaths) * 100)
text_size <- 12

# deaths
ggplot(data = pd,
       aes(x = ccaa,
           y = deaths)) +
  geom_bar(stat = 'identity',
           fill = 'black') +
  theme_simple() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(x = '',
       y = 'Deaths | Muertes',
       title = 'COVID-19 deaths in Spain',
       subtitle = paste0('Data as of ', max(pd$date)),
       caption = 'github.com/databrew/covid19 | joe@databrew.cc') +
  theme(legend.position = 'top',
        legend.text = element_text(size = text_size * 2),
        axis.title = element_text(size = text_size * 2),
        plot.title = element_text(size = text_size * 2.3),
        axis.text.x = element_text(size = text_size * 1.5)) +
  geom_text(data = pd %>% filter(deaths > 0),
            aes(x = ccaa,
                y = deaths,
                label = paste0(deaths, '\n(',
                               round(p, digits = 1), '%)')),
            size = text_size * 0.3,
            nudge_y = 180) +
  ylim(0, max(pd$deaths * 1.1))

ggsave('/tmp/spain.png')

Muertes relativas por CCAA

# Latest in Spain
pd <- esp_df %>%
  filter(date == max(date)) %>%
  mutate(p = deaths / sum(deaths) * 100)

pd <- pd %>% left_join(esp_pop)
text_size <- 12
pd$value <- pd$deaths / pd$pop * 100000

# deaths
ggplot(data = pd,
       aes(x = ccaa,
           y = value)) +
  geom_bar(stat = 'identity',
           fill = 'black') +
  theme_simple() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(x = '',
       y = 'Deaths per 100,000',
       title = 'COVID-19 deaths per 100.000',
       subtitle = paste0('Data as of ', max(pd$date)),
       caption = 'github.com/databrew/covid19 | joe@databrew.cc') +
  theme(legend.position = 'top',
        legend.text = element_text(size = text_size * 2),
        axis.title = element_text(size = text_size * 2),
        plot.title = element_text(size = text_size * 2.3),
        axis.text.x = element_text(size = text_size * 1.5)) +
  geom_text(data = pd %>% filter(value > 0),
            aes(x = ccaa,
                y = value,
                label = paste0(round(value, digits = 2), '\n(',
                               deaths, '\ndeaths)')),
            size = text_size * 0.3,
            nudge_y = 4.5) +
  ylim(0, max(pd$value) * 1.2)

ggsave('/tmp/spai2.png')

Just yesterday

# Latest in Spain
pd <- esp_df %>%
  filter(date == max(date)) %>%
  mutate(p = deaths_non_cum / sum(deaths_non_cum) * 100)
text_size <- 12

# deaths
ggplot(data = pd,
       aes(x = ccaa,
           y = deaths_non_cum)) +
  geom_bar(stat = 'identity',
           fill = 'black') +
  theme_simple() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(x = '',
       y = 'Deaths',
       title = 'COVID-19 deaths in Spain',
       subtitle = paste0('Data only for ', max(pd$date)),
       caption = 'github.com/databrew/covid19 | joe@databrew.cc') +
  theme(legend.position = 'top',
        legend.text = element_text(size = text_size * 2),
        axis.title = element_text(size = text_size * 2),
        plot.title = element_text(size = text_size * 2.3),
        axis.text.x = element_text(size = text_size * 1.5)) +
  geom_text(data = pd %>% filter(deaths_non_cum > 0),
            aes(x = ccaa,
                y = deaths_non_cum,
                label = paste0(deaths_non_cum, '\n(',
                               round(p, digits = 1), '%)')),
            size = text_size * 0.3,
            nudge_y = 30) +
  ylim(0, max(pd$deaths_non_cum * 1.1))

ggsave('/tmp/spain_non_cum.png')

Muertes relativas por CCAA ayer SOLO

# Latest in Spain
pd <- esp_df %>%
  filter(date == max(date)) %>%
  mutate(p = deaths_non_cum / sum(deaths_non_cum) * 100)

pd <- pd %>% left_join(esp_pop)
text_size <- 12
pd$value <- pd$deaths_non_cum / pd$pop * 100000

# deaths
ggplot(data = pd,
       aes(x = ccaa,
           y = value)) +
  geom_bar(stat = 'identity',
           fill = 'black') +
  theme_simple() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(x = '',
       y = 'Deaths per 100,000',
       title = 'COVID-19 deaths per 100.000',
       subtitle = paste0('Data as of ', max(pd$date)),
       caption = 'github.com/databrew/covid19 | joe@databrew.cc') +
  theme(legend.position = 'top',
        legend.text = element_text(size = text_size * 2),
        axis.title = element_text(size = text_size * 2),
        plot.title = element_text(size = text_size * 2.3),
        axis.text.x = element_text(size = text_size * 1.5)) +
  geom_text(data = pd %>% filter(value > 0),
            aes(x = ccaa,
                y = value,
                label = paste0(round(value, digits = 2), '\n(',
                               deaths_non_cum, '\ndeaths)')),
            size = text_size * 0.3,
            nudge_y = 1) +
  ylim(0, max(pd$value) * 1.3)

ggsave('/tmp/spain_ayer_adj.png')

plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(!ccaa %in% c('Melilla'))
roll_curve(plot_data, scales = 'fixed')

ggsave('/tmp/a.png',
       width = 1280 / 150,
       height = 720 / 150)

Loop for everywhere (see desktop)

dir.create('/tmp/ccaas')
ccaas <- sort(unique(esp_df$ccaa))
for(i in 1:length(ccaas)){
  this_ccaa <- ccaas[i]
  plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(ccaa == this_ccaa)
  roll_curve(plot_data, scales = 'fixed')  + theme(strip.text = element_text(size = 30))
  ggsave(paste0('/tmp/ccaas/', i, this_ccaa, '_cases.png'),
         width = 1280 / 150,
         height = 720 / 150)
}

ccaas <- sort(unique(esp_df$ccaa))
for(i in 1:length(ccaas)){
  this_ccaa <- ccaas[i]
  plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(ccaa == this_ccaa)
  roll_curve(plot_data, scales = 'fixed', pop = TRUE)  + theme(strip.text = element_text(size = 30))
  ggsave(paste0('/tmp/ccaas/', i, this_ccaa, '_cases_pop.png'),
         width = 1280 / 150,
         height = 720 / 150)
}

# Deaths too
for(i in 1:length(ccaas)){
  this_ccaa <- ccaas[i]
  plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(ccaa == this_ccaa)
  roll_curve(plot_data, deaths = T, scales = 'fixed') + theme(strip.text = element_text(size = 30))
  ggsave(paste0('/tmp/ccaas/', i, this_ccaa, '_deaths.png'),
         width = 1280 / 150,
         height = 720 / 150)
}

# Deaths too
for(i in 1:length(ccaas)){
  this_ccaa <- ccaas[i]
  plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(ccaa == this_ccaa)
  roll_curve(plot_data, deaths = T, scales = 'fixed', pop = TRUE)  + theme(strip.text = element_text(size = 30))
  ggsave(paste0('/tmp/ccaas/', i, this_ccaa, '_deaths_pop.png'),
         width = 1280 / 150,
         height = 720 / 150)
}

plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(!ccaa %in% c('Melilla'))
roll_curve(plot_data, scales = 'free_y')

ggsave('/tmp/b.png',
       width = 1280 / 150,
       height = 720 / 150)

plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(!ccaa %in% c('Melilla'))
roll_curve(plot_data, deaths = T, scales = 'free_y')

ggsave('/tmp/c.png',
       width = 1280 / 150,
       height = 720 / 150)

plot_data <- esp_df %>% mutate(geo = ccaa) %>% filter(!ccaa %in% c('Melilla'))
roll_curve(plot_data, deaths = T, scales = 'fixed')

ggsave('/tmp/d.png',
       width = 1280 / 150,
       height = 720 / 150)

plot_data <- df_country %>% filter(country %in% c('Spain', 'Italy', 'Germany', 'France', 'US',
                                                  'China', 'Korea, South', 'Japan', 'Singapore')) %>% mutate(geo = country)
roll_curve(plot_data, scales = 'free_y')

ggsave('/tmp/z.png',
       width = 1280 / 150,
       height = 720 / 150)

World at large

pd <- df_country %>%
  group_by(date) %>%
  summarise(n = sum(cases)) %>%
  filter(date < max(date))
ggplot(data = pd,
       aes(x = date,
           y = n)) +
  geom_point() +
  theme_simple() +
  labs(x = 'Date',
       y = 'Cases',
       title = 'COVID-19 cases')

ggsave('~/Videos/update/a.png',
       width = 1280 / 150,
       height = 720 / 150)

Error in grid.newpage(): could not open file '/home/joebrew/Videos/update/a.png'

China vs world

pd <- df_country %>%
  group_by(date,
           country = ifelse(country == 'China', 'China', 'Other countries')) %>%
  summarise(n = sum(cases))  %>%
  ungroup %>%
  filter(date < max(date))

Error: Column `country` can't be modified because it's a grouping variable

ggplot(data = pd,
       aes(x = date,
           y = n,
           color = country)) +
  geom_line(size = 2) +
  # geom_point() +
  theme_simple() +
  labs(x = 'Date',
       y = 'Cases',
       title = 'COVID-19 cases') +
  scale_color_manual(name = '',
                     values = c('red', 'black')) +
  theme(legend.text = element_text(size = 25),
        legend.position = 'top')

Error in FUN(X[[i]], ...): object 'country' not found

ggsave('~/Videos/update/b.png',
       width = 1280 / 150,
       height = 720 / 150)

Error in grid.newpage(): could not open file '/home/joebrew/Videos/update/b.png'

NOn china only

pd <- df_country %>%
  group_by(date,
           country = ifelse(country == 'China', 'China', 'Other countries')) %>%
  summarise(n = sum(cases)) %>%
  filter(country == 'Other countries')  %>%
  ungroup %>%
  filter(date < max(date))

Error: Column `country` can't be modified because it's a grouping variable

ggplot(data = pd,
       aes(x = date,
           y = n)) +
  geom_line(size = 2) +
  # geom_point() +
  theme_simple() +
  labs(x = 'Date',
       y = 'Cases',
       title = 'COVID-19 cases, outside of China') 

ggsave('~/Videos/update/c.png',
       width = 1280 / 150,
       height = 720 / 150)

Error in grid.newpage(): could not open file '/home/joebrew/Videos/update/c.png'

Case numbers across countries

plot_day_zero(countries = c('France', 'Germany', 'Italy', 'Spain', 'Switzerland', 'Sweden', 'Norway', 'Netherlands'))

# ggsave('~/Videos/update/d.png',
#        width = 1280 / 150,
#        height = 720 / 150)

World at large - deaths

pd <- df_country %>%
  group_by(date) %>%
  summarise(n = sum(deaths)) %>%
  filter(date < max(date))
ggplot(data = pd,
       aes(x = date,
           y = n)) +
  geom_point() +
  theme_simple() +
  labs(x = 'Date',
       y = 'Deaths',
       title = 'COVID-19 deaths')

# ggsave('~/Videos/update/e.png',
#        width = 1280 / 150,
#        height = 720 / 150)

China vs world deaths

pd <- df_country %>%
  group_by(date,
           country = ifelse(country == 'China', 'China', 'Other countries')) %>%
  summarise(n = sum(deaths))  %>%
  ungroup %>%
  filter(date < max(date))

Error: Column `country` can't be modified because it's a grouping variable

ggplot(data = pd,
       aes(x = date,
           y = n,
           color = country)) +
  geom_line(size = 2) +
  # geom_point() +
  theme_simple() +
  labs(x = 'Date',
       y = 'Deaths',
       title = 'COVID-19 deaths') +
  scale_color_manual(name = '',
                     values = c('red', 'black')) +
  theme(legend.text = element_text(size = 25),
        legend.position = 'top')

Error in FUN(X[[i]], ...): object 'country' not found

# ggsave('~/Videos/update/f.png',
#        width = 1280 / 150,
#        height = 720 / 150)

Asian hope

plot_day_zero(countries = c('Korea, South', 'Japan', 'China', 'Singapore'))

# ggsave('~/Videos/update/g.png',
#        width = 1280 / 150,
#        height = 720 / 150)

Since trajectories are very unstable when cases are low, we’ll exclude from our analysis the first few days, and will only count as “outbreak” once a country reaches 150 or more cumulative cases.

# Doubling time
n_cases_start = 150
countries = c('Italy', 'Spain', 'France', 'Germany', 'Italy', 'Switzerland', 'Denmark', 'US', 'United Kingdom', 'Norway')
# countries <- sort(unique(df_country$country))
out_list <- curve_list <-  list()
counter <- 0
for(i in 1:length(countries)){
  message(i)
  this_country <- countries[i]
  sub_data <-df_country %>% filter(country == this_country)
  # Only calculate on countries with n_cases_start or greater cases,
  # starting at the first day at n_cases_start or greater
  ok <- max(sub_data$cases, na.rm = TRUE) >= n_cases_start
  if(ok){
    counter <- counter + 1
    pd <- sub_data %>%
      filter(!is.na(cases)) %>%
      mutate(start_date = min(date[cases >= n_cases_start])) %>%
      mutate(days_since = date - start_date) %>%
      filter(days_since >= 0) %>%
      mutate(days_since = as.numeric(days_since))
    fit <- lm(log(cases) ~ days_since, data = pd) 
    # plot(pd$days_since, log(pd$cases))
    # abline(fit)
    ## Slope
    curve <- fit$coef[2]
    
    # Predict days ahead
    fake <- tibble(days_since = seq(0, max(pd$days_since) + 5, by = 1))
    fake <- left_join(fake, pd %>% dplyr::select(days_since, cases, date))
    fake$predicted <- exp(predict(fit, newdata = fake))
    
    # Doubling time
    dt <- log(2)/fit$coef[2]
    out <- tibble(country = this_country,
                  doubling_time = dt,
                  slope = curve)
    out_list[[counter]] <- out
    curve_list[[counter]] <- fake %>% mutate(country = this_country)
  }
}
done <- bind_rows(out_list)
print(done)

# A tibble: 10 x 3
   country        doubling_time  slope
   <chr>                  <dbl>  <dbl>
 1 Italy                   6.25 0.111 
 2 Spain                   5.03 0.138 
 3 France                  5.16 0.134 
 4 Germany                 5.18 0.134 
 5 Italy                   6.25 0.111 
 6 Switzerland             6.72 0.103 
 7 Denmark                 9.76 0.0710
 8 US                      3.77 0.184 
 9 United Kingdom          4.58 0.151 
10 Norway                 10.1  0.0687

curves <- bind_rows(curve_list)
# Get curves back in exponential form
# curves$curve <- exp(curves$curve)

# Join doubling time to curves
joined <- left_join(curves, done)

# Get rid of Italy future (since it's the "leader")
joined <- joined %>%
  filter(country != 'Italy' |
           date <= (Sys.Date() -1))


# Make long format
long <- joined %>% 
  dplyr::select(date, days_since, country, cases, predicted, doubling_time) %>%
  tidyr::gather(key, value, cases:predicted) %>%
  mutate(key = Hmisc::capitalize(gsub('_', ' ', key))) %>%
  mutate(key = ifelse(key == 'Predicted', 'Predicted (based on current doubling time)', key))

The below chart shows the trajectories in terms of number of cases in Europe in red, and the predicted trajectories in black. The black line assumes that the doubling rate will stay constant.

cols <- c('red', 'black')
ggplot(data = long,
       aes(x = days_since,
           y = value,
           lty = key,
           color = key)) +
  geom_line(data = long %>% filter(key != 'Confirmed cases'),
            size = 1.2, alpha = 0.8) +
  geom_point(data = long %>% filter(key == 'Confirmed cases')) +
  geom_line(data = long %>% filter(key == 'Confirmed cases'),
            size = 0.8) +
  facet_wrap(~paste0(country, '\n',
                     '(doubling time: ', 
                     round(doubling_time, digits = 1), ' days)'), scales = 'free') +
  theme_simple() +
  scale_linetype_manual(name ='',
                        values = c(1,2)) +
  scale_color_manual(name = '',
                     values = cols) +
  theme(legend.position = 'top') +
  labs(x = 'Days since first day at >150 cumulative cases',
       y = 'Cases',
       title = 'COVID-19 CASES: ("predicted" assumes no change in doubling time)',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Doubling time calculated since first day at >150 cumulative cases)') +
    theme(strip.text = element_text(size = 13),
          plot.title = element_text(size = 15))

Since Italy is “leading the way”, it’s helpful to also compare each country to Italy. Let’s see that.

# Overlay Italy
ol1 <- joined %>% filter(!country %in% 'Italy')
ol2 <- joined %>% filter(country == 'Italy') %>% dplyr::rename(Italy = cases) %>%
  dplyr::select(Italy, days_since)
ol <- left_join(ol1, ol2) %>%
  dplyr::select(days_since, date, country, cases, predicted, Italy,doubling_time)
ol <- tidyr::gather(ol, key, value, cases: Italy) %>%
  mutate(key = Hmisc::capitalize(gsub('_', ' ', key))) %>%
  mutate(key = ifelse(key == 'Predicted', 'Predicted (based on current doubling time)', key))

cols <- c('red', 'blue', 'black')
ggplot(data = ol,
       aes(x = days_since,
           y = value,
           lty = key,
           color = key)) +
  geom_line(data = ol %>% filter(!key %in% c('Confirmed cases', 'Italy')),
            size = 1.2, alpha = 0.8) +
    geom_line(data = ol %>% filter(key %in% c('Italy')),
            size = 0.8, alpha = 0.8) +
  geom_point(data = ol %>% filter(key == 'Confirmed cases')) +
  geom_line(data = ol %>% filter(key == 'Confirmed cases'),
            size = 0.8) +
  facet_wrap(~paste0(country, '\n',
                     '(doubling time: ', 
                     round(doubling_time, digits = 1), ' days)'), scales = 'free') +
  theme_simple() +
  scale_linetype_manual(name ='',
                        values = c(1,6,2)) +
  scale_color_manual(name = '',
                     values = cols) +
  theme(legend.position = 'top') +
  labs(x = 'Days since first day at >150 cumulative cases',
       y = 'Cases',
       title = 'COVID-19 CASES: ("predicted" assumes no change in doubling time)',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Doubling time calculated since first day at >150 cumulative cases)') +
    theme(strip.text = element_text(size = 13),
          plot.title = element_text(size = 15))

In the above, what’s striking is how many places have trajectories that are worse than Italy’s. Yes, Italy has more cases, but it’s doubling time is less. Either that changes soon, or these other countries will soon have more cases than Italy.

Deaths or cases?

The number of cases is not necessarily the best indicator for the severity of an outbreak of this nature. Why? Because (a) testing rates and protocols are different by place and (b) testing rates are different by time (since health services are changing their approaches as things develop). In other words, when we compare the number of cases by place and time, we are introducing significant bias.

Using deaths to gauge the magnitude of the outbreak is also problematic. Death rates are differential by age, so the number of deaths depends on a country’s population period, or age structure. Also, death rates will be a function of health services, which are not of the same quality every where. And, of course, like cases, we don’t necessarily know about all of the deaths that occur because of COVID-19.

Still, there’s an argument that death rates have less bias than case rates because deaths are easier to identify than cases. Let’s accept that argument, for the time being, and have a look at death rates by country.

# Doubling time
n_deaths_start = 5
countries = c('Italy', 'Spain', 'France', 'Italy', 'Switzerland', 'Denmark', 'US', 'United Kingdom', 'Norway', 'Germany')
# countries <- sort(unique(df_country$country))

make_double_time <- function(data = df_country,
                             the_country = 'Spain',
                             n_deaths_start = 5,
                             time_ahead = 7){
   sub_data <-data %>% filter(country == the_country)
  # Only calculate on countries with n_cases_start or greater cases,
  # starting at the first day at n_cases_start or greater
  ok <- max(sub_data$deaths, na.rm = TRUE) >= n_deaths_start
  if(ok){
    counter <- counter + 1
    pd <- sub_data %>%
      filter(!is.na(deaths)) %>%
      mutate(start_date = min(date[deaths >= n_deaths_start])) %>%
      mutate(days_since = date - start_date) %>%
      filter(days_since >= 0) %>%
      mutate(days_since = as.numeric(days_since)) %>%
      mutate(the_weight = 1/(1 + (as.numeric(max(date) - date))))
    fit <- lm(log(deaths) ~ days_since,
              weights = the_weight,
              data = pd) 
    # fitlo <- loess(deaths ~ days_since, data = pd)
    # plot(pd$days_since, log(pd$cases))
    # abline(fit)
    ## Slope
    # curve <- fit$coef[2]
    
    # Predict days ahead
    day0 <- pd$date[pd$days_since == 0]
    fake <- tibble(days_since = seq(0, max(pd$days_since) + time_ahead, by = 1))
    fake <- fake %>%mutate(date = seq(day0, day0+max(fake$days_since), by = 1))
    fake <- left_join(fake, pd %>% dplyr::select(days_since, deaths, date))
    fake$predicted <- exp(predict(fit, newdata = fake))
    # fake$predictedlo <- predict(fitlo, newdata = fake)
    ci <- exp(predict(fit, newdata = fake, interval = 'prediction'))
    # cilo <- predict(fitlo, newdata = fake, interval = 'prediction')

    fake$lwr <- ci[,'lwr']
    fake$upr <- ci[,'upr']
    # fake$lwrlo <- ci[,'lwr']
    # fake$uprlo <- ci[,'upr']
    # Doubling time
    dt <- log(2)/fit$coef[2]
    fake %>% mutate(country = the_country) %>% mutate(doubling_time = dt)
  }
}

plot_double_time <- function(data, ylog = F){
  the_labs <- labs(x = 'Date',
                   y = 'Deaths',
                   title = paste0('Predicted deaths in ', data$country[1]))
  long <- data %>%
    tidyr::gather(key, value, deaths:predicted) %>%
    mutate(key = Hmisc::capitalize(key))
  g <- ggplot() +
        geom_ribbon(data = data %>% filter(date > max(long$date[!is.na(long$value) & long$key == 'Deaths'])),
                aes(x = date,
                    ymax = upr,
                    ymin = lwr),
                alpha =0.6,
                fill = 'darkorange') +
    geom_line(data = long,
              aes(x = date,
                  y = value,
                  group = key,
                  lty = key)) +
    geom_point(data = long %>% filter(key == 'Deaths'),
               aes(x = date,
                   y = value)) +
    theme_simple() +
    theme(legend.position = 'right',
          legend.title = element_blank()) +
    the_labs
  if(ylog){
    g <- g + scale_y_log10()
  }
  return(g)
}
options(scipen = '999')
data <- make_double_time(n_deaths_start = 150, time_ahead = 7)
data

# A tibble: 44 x 8
   days_since date       country deaths predicted   lwr   upr doubling_time
        <dbl> <date>     <chr>    <dbl>     <dbl> <dbl> <dbl>         <dbl>
 1          0 2020-03-15 Spain      288     1650. 1072. 2539.          9.08
 2          1 2020-03-16 Spain      491     1781. 1168. 2714.          9.08
 3          2 2020-03-17 Spain      598     1922. 1273. 2902.          9.08
 4          3 2020-03-18 Spain      767     2074. 1387. 3103.          9.08
 5          4 2020-03-19 Spain     1002     2239. 1511. 3319.          9.08
 6          5 2020-03-20 Spain     1326     2417. 1645. 3550.          9.08
 7          6 2020-03-21 Spain     1720     2608. 1791. 3798.          9.08
 8          7 2020-03-22 Spain     2182     2815. 1950. 4064.          9.08
 9          8 2020-03-23 Spain     2696     3039. 2122. 4350.          9.08
10          9 2020-03-24 Spain     3434     3280. 2310. 4657.          9.08
# … with 34 more rows

dir.create('/tmp/ccaa_predictions')

plot_double_time(data, ylog = T) +
  labs(subtitle = 'Basic log-linear model weighted at (1 + (1/ days ago)),\nassuming no change in growth trajectory since first day at >150 deaths')

ggsave('/tmp/ccaa_predictions/spain.png')
# All ccaas
ccaas <- sort(unique(esp_df$ccaa))
for(i in 1:length(ccaas)){
  message(i)
  this_ccaa <- ccaas[i]
  sub_data <- esp_df %>% mutate(country = ccaa) 
  try({
    data <- make_double_time(
    data = sub_data,
    the_country = this_ccaa,
    n_deaths_start = 5,
    time_ahead = 7)
  plot_double_time(data, ylog = T) +
  labs(subtitle = 'Basic log-linear model weighted at (1 + (1/ days ago)), assuming no change in growth trajectory since first day at >5 deaths')
  ggsave(paste0('/tmp/ccaa_predictions/',
                this_ccaa, '.png'),
         height = 4.9,
         width = 8.5)
  })

}

Error in UseMethod("gather_") : 
  no applicable method for 'gather_' applied to an object of class "NULL"

Error in UseMethod("gather_") : 
  no applicable method for 'gather_' applied to an object of class "NULL"

# all_countries <- sort(unique(df_country$country))
# for(i in 1:length(all_countries)){
#   this_country <- all_countries[i]
#   data <- make_double_time(the_country = this_country, n_deaths_start = 5)
#   if(!is.null(data)){
#     # print(this_country)
#     g <- plot_double_time(data, ylog = F) +
#   labs(subtitle = 'Basic log-linear model assuming no change in growth trajectory since first day at >5 deaths')
#     ggsave(paste0('/tmp/', this_country, '.png'), height = 5, width = 8)
#     print(data)
#   }
# }

out_list <- curve_list <-  list()
counter <- 0
for(i in 1:length(countries)){
  message(i)
  this_country <- countries[i]
  sub_data <-df_country %>% filter(country == this_country)
  # Only calculate on countries with n_cases_start or greater cases,
  # starting at the first day at n_cases_start or greater
  ok <- max(sub_data$deaths, na.rm = TRUE) >= n_deaths_start
  if(ok){
    counter <- counter + 1
    pd <- sub_data %>%
      filter(!is.na(deaths)) %>%
      mutate(start_date = min(date[deaths >= n_deaths_start])) %>%
      mutate(days_since = date - start_date) %>%
      filter(days_since >= 0) %>%
      mutate(days_since = as.numeric(days_since))
    fit <- lm(log(deaths) ~ days_since, data = pd) 
    # plot(pd$days_since, log(pd$cases))
    # abline(fit)
    ## Slope
    # curve <- fit$coef[2]
    
    # Predict days ahead
    fake <- tibble(days_since = seq(0, max(pd$days_since) + 5, by = 1))
    fake <- left_join(fake, pd %>% dplyr::select(days_since, deaths, date))
    fake$predicted <- exp(predict(fit, newdata = fake))
    
    # Doubling time
    dt <- log(2)/fit$coef[2]
    out <- tibble(country = this_country,
                  doubling_time = dt)
    out_list[[counter]] <- out
    curve_list[[counter]] <- fake %>% mutate(country = this_country)
  }
}

Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...): NA/NaN/Inf in 'y'

done <- bind_rows(out_list)
curves <- bind_rows(curve_list)
# Get curves back in exponential form
# curves$curve <- exp(curves$curve)

# Join doubling time to curves
joined <- left_join(curves, done)

# Get rid of Italy future (since it's the "leader")
joined <- joined %>%
  filter(country != 'Italy' |
           date <= (Sys.Date() -1))


# Make long format
long <- joined %>% 
  dplyr::select(date, days_since, country, deaths, predicted, doubling_time) %>%
  tidyr::gather(key, value, deaths:predicted) %>%
  mutate(key = Hmisc::capitalize(gsub('_', ' ', key))) %>%
  mutate(key = ifelse(key == 'Predicted', 'Predicted (based on current doubling time)', key))

cols <- c('red', 'black')
sub_data <-  long %>% filter(country != 'US')
ggplot(data = sub_data,
       aes(x = days_since,
           y = value,
           lty = key,
           color = key)) +
  geom_line(data = sub_data %>% filter(key != 'Deaths'),
            size = 1.2, alpha = 0.8) +
  geom_point(data = sub_data %>% filter(key == 'Deaths')) +
  geom_line(data = sub_data %>% filter(key == 'Deaths'),
            size = 0.8) +
  facet_wrap(~paste0(country, '\n',
                     '(doubling time: ', 
                     round(doubling_time, digits = 1), ' days)'), scales = 'free') +
  theme_simple() +
  scale_linetype_manual(name ='',
                        values = c(1,2)) +
  scale_color_manual(name = '',
                     values = cols) +
  theme(legend.position = 'top') +
  labs(x = 'Days since first day at >5 cumulative deaths',
       y = 'Deaths',
       title = 'COVID-19 DEATHS: ("predicted" assumes no change in doubling time)',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Doubling time calculated since first day at >5 cumulative deaths)') +
    theme(strip.text = element_text(size = 13),
          plot.title = element_text(size = 15))

Let’s again overlay Italy.

# Overlay Italy
ol1 <- joined %>% filter(!country %in% 'Italy')
ol2 <- joined %>% filter(country == 'Italy') %>% dplyr::rename(Italy = deaths) %>%
  dplyr::select(Italy, days_since)
ol <- left_join(ol1, ol2) %>%
  dplyr::select(days_since, date, country, deaths, predicted, Italy,doubling_time)
ol <- tidyr::gather(ol, key, value, deaths: Italy) %>%
  mutate(key = Hmisc::capitalize(gsub('_', ' ', key))) %>%
  mutate(key = ifelse(key == 'Predicted', 'Predicted (based on current doubling time)', key))

cols <- c('red', 'blue', 'black')
sub_data <- ol %>% 
  filter(!(key == 'Predicted (based on current doubling time)' &
             country == 'Spain' &
             days_since > 13))
ggplot(data = sub_data,
       aes(x = days_since,
           y = value,
           lty = key,
           color = key)) +
  geom_line(data = sub_data %>% filter(!key %in% c('Deaths', 'Italy')),
            size = 1.2, alpha = 0.8) +
    geom_line(data = sub_data %>% filter(key %in% c('Italy')),
            size = 0.8, alpha = 0.8) +
  geom_point(data = sub_data %>% filter(key == 'Deaths')) +
  geom_line(data = sub_data %>% filter(key == 'Deaths'),
            size = 0.8) +
  facet_wrap(~paste0(country, '\n',
                     '(doubling time: ', 
                     round(doubling_time, digits = 1), ' days)'), scales = 'free') +
  theme_simple() +
  scale_linetype_manual(name ='',
                        values = c(1,6,2)) +
  scale_color_manual(name = '',
                     values = cols) +
  scale_y_log10() +
  theme(legend.position = 'top') +
  labs(x = 'Days since first day at >5 deaths',
       y = 'Deaths',
       title = 'COVID-19 DEATHS: ("predicted" assumes no change in doubling time)',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Doubling time calculated since first day at >5 cumulative deaths)') +
    theme(strip.text = element_text(size = 13),
          plot.title = element_text(size = 15)) 

Let’s look just at Spain

# Overlay Italy
ol1 <- joined %>% filter(!country %in% 'Italy',
                         country == 'Spain')
ol2 <- joined %>% filter(country == 'Italy') %>% dplyr::rename(Italy = deaths) %>%
  dplyr::select(Italy, days_since)
ol <- left_join(ol1, ol2) %>%
  dplyr::select(days_since, date, country, deaths, predicted, Italy,doubling_time)
ol <- tidyr::gather(ol, key, value, deaths: Italy) %>%
  mutate(key = Hmisc::capitalize(gsub('_', ' ', key))) %>%
  mutate(key = ifelse(key == 'Predicted', 'Predicted (based on current doubling time)', 
                      ifelse(key == 'Deaths', 'Spain', key)))

cols <- c('blue',  'black', 'red')
ggplot(data = ol,
       aes(x = days_since,
           y = value,
           lty = key,
           color = key)) +
  geom_line(data = ol %>% filter(!key %in% c('Deaths', 'Italy')),
            size = 1.2, alpha = 0.8) +
    geom_line(data = ol %>% filter(key %in% c('Italy')),
            size = 0.8, alpha = 0.8) +
  # geom_point(data = ol %>% filter(key == 'Deaths')) +
    geom_point(data = ol %>% filter(country == 'Spain',
                                    key == 'Spain'), size = 4, alpha = 0.6) +

  geom_line(data = ol %>% filter(key == 'Deaths'),
            size = 0.8) +
  # facet_wrap(~paste0(country, '\n',
  #                    '(doubling time: ', 
  #                    round(doubling_time, digits = 1), ' days)'), scales = 'free') +
  theme_simple() +
  scale_linetype_manual(name ='',
                        values = c(1,6,1)) +
  scale_color_manual(name = '',
                     values = cols) +
  scale_y_log10() +
  theme(legend.position = 'top') +
  labs(x = 'Days since first day at >5 deaths',
       y = 'Deaths',
       title = 'COVID-19 DEATHS: ("predicted" assumes no change in doubling time)',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Doubling time calculated since first day at >5 cumulative deaths)') +
    theme(strip.text = element_text(size = 13),
          plot.title = element_text(size = 15),
          axis.title = element_text(size = 18))

The importance of lag

Things are changing very rapidly. And measures being taken by these countries will have an impact on the outbreak.

But it’s important to remember that there is a lag between when an intervention takes place and when its effect is notable. Because of the incubation period - the number of days between someone getting infected and becoming sick - what we do today won’t really have an effect until next weekend. And the clinical cases that present today are among people who got infected a week ago.

Disease control measures work. We can see that clearly in the case of Hubei, Wuhan, Iran, Japan. And they will work in Europe too. But because many of these measures were implemented very recently, we won’t likely see a major effect for at least a few more days.

In the mean time, it’s important to practice social distancing. Stay away from others to keep both you and others safe. Listen to Health Authorities. Take this very seriously.

Spain and Italy regions

# Madrid vs Lombardy deaths
n_death_start <- 5
pd <- esp_df %>%
  # filter(ccaa == 'Madrid') %>%
  dplyr::select(date, ccaa, cases, deaths) %>%
  bind_rows(ita %>%
              # filter(ccaa == 'Lombardia') %>%
              dplyr::select(date, ccaa, cases, deaths)) %>%
  arrange(date) %>%
  group_by(ccaa) %>%
  mutate(first_n_death = min(date[deaths >= n_death_start])) %>%
  ungroup %>%
  mutate(days_since_n_deaths = date - first_n_death) %>%
  filter(is.finite(days_since_n_deaths))

pd$country <- pd$ccaa
pd$cases <- pd$cases
countries <- sort(unique(pd$country))
out_list <- curve_list <-  list()
counter <- 0
for(i in 1:length(countries)){
  message(i)
  this_country <- countries[i]
  sub_data <- pd %>% filter(country == this_country)
  # Only calculate on countries with n_cases_start or greater cases,
  # starting at the first day at n_cases_start or greater
  # ok <- max(sub_data$deaths, na.rm = TRUE) >= n_deaths_start
  ok <- length(which(sub_data$deaths >= n_deaths_start))
  if(ok){
    counter <- counter + 1
    sub_pd <- sub_data %>%
      filter(!is.na(deaths)) %>%
      mutate(start_date = min(date[deaths >= n_deaths_start])) %>%
      mutate(days_since = date - start_date) %>%
      filter(days_since >= 0) %>%
      mutate(days_since = as.numeric(days_since))
    fit <- lm(log(deaths) ~ days_since, data = sub_pd) 
    # plot(pd$days_since, log(pd$cases))
    # abline(fit)
    ## Slope
    # curve <- fit$coef[2]
    
    # Predict days ahead
    fake <- tibble(days_since = seq(0, max(sub_pd$days_since) + 5, by = 1))
    fake <- left_join(fake, sub_pd %>% dplyr::select(days_since, deaths, date))
    fake$predicted <- exp(predict(fit, newdata = fake))
    
    # Doubling time
    dt <- log(2)/fit$coef[2]
    out <- tibble(country = this_country,
                  doubling_time = dt)
    out_list[[counter]] <- out
    curve_list[[counter]] <- fake %>% mutate(country = this_country)
  }
}
done <- bind_rows(out_list)
curves <- bind_rows(curve_list)
# Get curves back in exponential form
# curves$curve <- exp(curves$curve)

# Join doubling time to curves
joined <- left_join(curves, done)


# Make long format
long <- joined %>% 
  dplyr::select(date, days_since, country, deaths, predicted, doubling_time) %>%
  tidyr::gather(key, value, deaths:predicted) %>%
  mutate(key = Hmisc::capitalize(gsub('_', ' ', key))) %>%
  mutate(key = ifelse(key == 'Predicted', 'Predicted (based on current doubling time)', key))

# Remove those with not enough data to have a doubling time yet
long <- long %>% filter(!is.na(doubling_time))

text_size <- 12

cols <- c('red', 'black')
ggplot(data = long,
       aes(x = days_since,
           y = value,
           lty = key,
           color = key)) +
  geom_line(data = long %>% filter(key != 'Deaths'),
            size = 1.2, alpha = 0.8) +
  geom_point(data = long %>% filter(key == 'Deaths')) +
  geom_line(data = long %>% filter(key == 'Deaths'),
            size = 0.8) +
  facet_wrap(~paste0(country, '\n',
                     '(doubling time: ', 
                     round(doubling_time, digits = 1), ' days)'), scales = 'free') +
  theme_simple() +
  scale_y_log10() +
  scale_linetype_manual(name ='',
                        values = c(1,2)) +
  scale_color_manual(name = '',
                     values = cols) +
  theme(legend.position = 'top') +
  labs(x = 'Days since first day at >150 cumulative cases',
       y = 'Deaths',
       title = 'COVID-19 DEATHS: ("predicted" assumes no change in doubling time)',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Doubling time calculated since first day at >5 cumulative deaths)') +
    theme(strip.text = element_text(size = text_size * 0.5),
          plot.title = element_text(size = 15))

Let’s overlay Lombardy

# Overlay Italy
ol1 <- joined %>% filter(!country %in% 'Lombardia')
ol2 <- joined %>% filter(country == 'Lombardia') %>% dplyr::rename(Lombardia = deaths) %>%
  dplyr::select(Lombardia, days_since)
ol <- left_join(ol1, ol2) %>%
  dplyr::select(days_since, date, country, deaths, predicted, Lombardia,doubling_time)
ol <- tidyr::gather(ol, key, value, deaths: Lombardia) %>%
  mutate(key = Hmisc::capitalize(gsub('_', ' ', key))) %>%
  mutate(key = ifelse(key == 'Predicted', 'Predicted (based on current doubling time)', key))

# Remove those with not enough data to have a doubling time yet
ol <- ol %>% filter(!is.na(doubling_time))

cols <- c('red', 'blue', 'black')
ggplot(data = ol,
       aes(x = days_since,
           y = value,
           lty = key,
           color = key)) +
  scale_y_log10() +
  geom_line(data = ol %>% filter(!key %in% c('Deaths', 'Italy')),
            size = 1.2, alpha = 0.8) +
    geom_line(data = ol %>% filter(key %in% c('Lombardia')),
            size = 0.5, alpha = 0.8) +
  geom_point(data = ol %>% filter(key == 'Deaths')) +
  geom_line(data = ol %>% filter(key == 'Deaths'),
            size = 0.8) +
  facet_wrap(~paste0(country, '\n',
                     '(doubling time: ', 
                     round(doubling_time, digits = 1), ' days)'), scales = 'free') +
  theme_simple() +
  scale_linetype_manual(name ='',
                        values = c(1,6,2)) +
  scale_color_manual(name = '',
                     values = cols) +
  theme(legend.position = 'top') +
  labs(x = 'Days since first day at >5 deaths',
       y = 'Deaths',
       title = 'COVID-19 DEATHS: ("predicted" assumes no change in doubling time)',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Doubling time calculated since first day at >5 cumulative deaths)') +
    theme(strip.text = element_text(size = text_size * 0.5),
          plot.title = element_text(size = 15))

Show only Spanish regions vs. Lombardy

text_size <- 14

# Overlay Italy
ol1 <- joined %>% filter(!country %in% 'Lombardia')
ol2 <- joined %>% filter(country == 'Lombardia') %>% dplyr::rename(Lombardia = deaths) %>%
  dplyr::select(Lombardia, days_since)
ol <- left_join(ol1, ol2) %>%
  dplyr::select(days_since, date, country, deaths, predicted, Lombardia,doubling_time)
ol <- tidyr::gather(ol, key, value, deaths: Lombardia) %>%
  mutate(key = Hmisc::capitalize(gsub('_', ' ', key))) %>%
  mutate(key = ifelse(key == 'Predicted', 'Predicted (based on current doubling time)', key))

# Remove those with not enough data to have a doubling time yet
ol <- ol %>% filter(!is.na(doubling_time))

# Only Spain
ol <- ol %>% filter(country %in% esp_df$ccaa) %>%
  filter(!country %in% 'Aragón')

cols <- c('red', 'blue', 'black')
ggplot(data = ol,
       aes(x = days_since,
           y = value,
           lty = key,
           color = key)) +
  scale_y_log10() +
  geom_line(data = ol %>% filter(!key %in% c('Deaths', 'Lombardia')),
            size = 1.2, alpha = 0.8) +
    geom_line(data = ol %>% filter(key %in% c('Lombardia')),
            size = 0.5, alpha = 0.8) +
  geom_point(data = ol %>% filter(key == 'Deaths')) +
  geom_line(data = ol %>% filter(key == 'Deaths'),
            size = 0.8) +
  facet_wrap(~paste0(country, '\n',
                     '(doubling time: ', 
                     round(doubling_time, digits = 1), ' days)'), scales = 'free') +
  theme_simple() +
  scale_linetype_manual(name ='',
                        values = c(1,6,2)) +
  scale_color_manual(name = '',
                     values = cols) +
  theme(legend.position = 'top') +
  labs(x = 'Days since first day at >5 deaths',
       y = 'Deaths',
       title = 'COVID-19 DEATHS: ("predicted" assumes no change in doubling time)',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Doubling time calculated since first day at >5 cumulative deaths)') +
    theme(strip.text = element_text(size = text_size * 0.6),
          plot.title = element_text(size = 15))

Same plot but overlayed

Same as above, but overlaid

text_size <-10

# cols <- c('red', 'black')
long <- long %>% filter(country %in% c('Lombardia',
                                       'Emilia Romagna') |
                          country %in% esp_df$ccaa) %>%
  filter(country != 'Aragón')
places <- sort(unique(long$country))

cols <- colorRampPalette(RColorBrewer::brewer.pal(n = 7, 'Spectral'))(length(places))
cols[which(places == 'Madrid')] <- 'red'
cols[which(places == 'Cataluña')] <- 'purple'
cols[which(places == 'Lombardia')] <- 'darkorange'
cols[which(places == 'Emilia Romagna')] <- 'darkgreen'

long$key <- ifelse(long$key != 'Deaths', 'Predicted', long$key)
long$key <- ifelse(long$key == 'Predicted', 'Muertes\nprevistas',
                   'Muertes\nobservadas')


# Keep only Madrid, Lombardy, Emilia Romagna
long <- long %>%
  filter(country %in% c('Madrid',
                        'Lombardia',
                        'Emilia Romagna'))

ggplot(data = long,
       aes(x = days_since,
           y = value,
           lty = key,
           color = country)) +
  geom_point(data = long %>% filter(key == 'Muertes\nobservadas'), size = 2, alpha = 0.8) +
  geom_line(data = long %>% filter(key == 'Muertes\nprevistas'), size = 1, alpha = 0.7) +
  geom_line(data = long %>% filter(key != 'Muertes\nprevistas'), size = 0.8) +
  theme_simple() +
  scale_y_log10() +
  scale_linetype_manual(name ='',
                        values = c(1,4)) +
  scale_color_manual(name = '',
                     values = cols) +
  theme(legend.position = 'top') +
  # labs(x = 'Days since first day at 5 or more cumulative deaths',
  #      y = 'Deaths',
  #      title = 'COVID-19 DEATHS: ("predicted" assumes no change in doubling time)',
  #      caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
  #      subtitle = '(Doubling time calculated since first day at >5 cumulative deaths)') +
    labs(x = 'Días desde el primer día a 5 o más muertes acumuladas',
       y = 'Muertes (escala logarítmica)',
       title = 'Muertes por COVID-19',
       caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
       subtitle = '(Tasa de crecimiento calculada desde el primer día a 5 o más muertes acumuladas)\n(Muertes "previstas": suponiendo que no hay cambios en la tasa de crecimiento)') +
    theme(strip.text = element_text(size = text_size * 0.75),
          plot.title = element_text(size = text_size * 3),
          legend.text = element_text(size = text_size * 1.5),
          axis.title = element_text(size = text_size * 2),
          axis.text = element_text(size = text_size * 2))

# cols <- c(cols, 'darkorange')
# ggplot(data = ol,
#        aes(x = days_since,
#            y = value,
#            lty = key,
#            color = key)) +
#   scale_y_log10() +
#   geom_line(aes(color = country)) +
#   
#   # geom_line(data = ol %>% filter(!key %in% c('Deaths', 'Italy')),
#   #           size = 1.2, alpha = 0.8) +
#   #   geom_line(data = ol %>% filter(key %in% c('Lombardia')),
#   #           size = 0.5, alpha = 0.8) +
#   # geom_point(data = ol %>% filter(key == 'Deaths')) +
#   # geom_line(data = ol %>% filter(key == 'Deaths'),
#   #           size = 0.8) +
#   theme_simple() +
#   scale_linetype_manual(name ='',
#                         values = c(1,6,2)) +
#   scale_color_manual(name = '',
#                      values = cols) +
#   theme(legend.position = 'top') +
#   labs(x = 'Days since first day at >5 deaths',
#        y = 'Deaths',
#        title = 'COVID-19 DEATHS: ("predicted" assumes no change in doubling time)',
#        caption = 'Data from Johns Hopkins. Processing: Joe Brew @joethebrew. Code: github.com/databrew/covid19',
#        subtitle = '(Doubling time calculated since first day at >5 cumulative deaths)') +
#     theme(strip.text = element_text(size = text_size * 1),
#           plot.title = element_text(size = 15))

# Map data preparation

if(!'map.RData' %in% dir()){
  esp1 <- getData(name = 'GADM', country = 'ESP', level = 1)
# Remove canary
esp1 <- esp1[esp1@data$NAME_1 != 'Islas Canarias',]
espf <- fortify(esp1, region = 'NAME_1')

# Standardize names
# Convert names
map_names <- esp1@data$NAME_1
data_names <- sort(unique(esp_df$ccaa))
names_df <- tibble(NAME_1 = c('Andalucía',
 'Aragón',
 'Cantabria',
 'Castilla-La Mancha',
 'Castilla y León',
 'Cataluña',
 'Ceuta y Melilla',
 'Comunidad de Madrid',
 'Comunidad Foral de Navarra',
 'Comunidad Valenciana',
 'Extremadura',
 'Galicia',
 'Islas Baleares',
 'La Rioja',
 'País Vasco',
 'Principado de Asturias',
 'Región de Murcia'),
 ccaa = c('Andalucía',
 'Aragón',
 'Cantabria',
 'CLM',
 'CyL',
 'Cataluña',
 'Melilla',
 'Madrid',
 'Navarra',
 'C. Valenciana',
 'Extremadura',
 'Galicia',
 'Baleares',
 'La Rioja',
 'País Vasco',
 'Asturias',
 'Murcia'))


espf <- left_join(espf %>% dplyr::rename(NAME_1 = id), names_df)
centroids <- data.frame(coordinates(esp1))
names(centroids) <- c('long', 'lat')
centroids$NAME_1 <- esp1$NAME_1
centroids <- centroids %>% left_join(names_df)

# Get random sampling points

  random_list <- list()
  for(i in 1:nrow(esp1)){
    message(i)
    shp <- esp1[i,]
    # bb <- bbox(shp)
    this_ccaa <- esp1@data$NAME_1[i]
    # xs <- runif(n = 500, min = bb[1,1], max = bb[1,2])
    # ys <- runif(n = 500, min = bb[2,1], max = bb[2,2])
    # random_points <- expand.grid(long = xs, lat = ys) %>%
    #   mutate(x = long,
    #          y = lat)
    # coordinates(random_points) <- ~x+y
    # proj4string(random_points) <- proj4string(shp)
    # get ccaa
    message('getting locations of randomly generated points')
    # polys <- over(random_points,polygons(shp))
    # polys <- as.numeric(polys)
    random_points <- spsample(shp, n = 20000, type = 'random')
    random_points <- data.frame(random_points)
    random_points$NAME_1 <-  this_ccaa
    random_points <- left_join(random_points, names_df) %>% dplyr::select(-NAME_1)
    random_list[[i]] <- random_points
  }
  random_points <- bind_rows(random_list)
  random_points <- random_points %>% mutate(long = x,
                                            lat = y)

save(espf,
     esp1,
     names_df,
     centroids,
     random_points,
     file = 'map.RData')
} else {
  load('map.RData')
}

# Define a function for adding zerio
add_zero <- 
  function (x, n) 
  {
    x <- as.character(x)
    adders <- n - nchar(x)
    adders <- ifelse(adders < 0, 0, adders)
    for (i in 1:length(x)) {
      if (!is.na(x[i])) {
        x[i] <- paste0(paste0(rep("0", adders[i]), collapse = ""), 
                       x[i], collapse = "")
      }
    }
    return(x)
  }

remake_world_map <- FALSE
options(scipen = '999')
if(remake_world_map){
  # World map animation
  world <- map_data('world')
  # world <- ne_countries(scale = "medium", returnclass = "sf")
  
  # Get plotting data
  pd <- df_country %>%
    dplyr::select(date, lng, lat, n = cases)
  dates <- sort(unique(pd$date))
  n_days <- length(dates)
  # # Define vectors for projection
  # vec_lon <- seq(30, -20, length = n_days)
  # vec_lat <- seq(25, 15, length = n_days)
  
  dir.create('animation')
  for(i in 1:n_days){
    message(i, ' of ', n_days)
    this_date <- dates[i]
    # this_lon <- vec_lon[i]
    # this_lat <- vec_lat[i]
    # the_crs <-
    #   paste0("+proj=laea +lat_0=", this_lat,
    #          " +lon_0=",
    #          this_lon,
    #          " +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defs ")
    sub_data <- pd %>%
      filter(date == this_date)
    # coordinates(sub_data) <- ~lng+lat
    # proj4string(sub_data) <- proj4string(esp1)
    # # sub_data <- spTransform(sub_data,
    # #                         the_crs)
    # coordy <- coordinates(sub_data)
    # sub_data@data$long <- coordy[,1]
    # sub_data@data$lat <- coordy[,2]
  
    g <- ggplot() +
      geom_polygon(data = world,
                   aes(x = long,
                       y = lat,
                       group = group),
                   fill = 'black',
                   color = 'white',
                   size = 0.1) +
      theme_map() +
          geom_point(data = sub_data %>% filter(n > 0) %>% mutate(Deaths = n),
                 aes(x = lng,
                     y = lat,
                     size = Deaths),
                 color = 'red',
                 alpha = 0.6) +
      geom_point(data = tibble(x = c(0,0), y = c(0,0), Deaths = c(1, 100000)),
                 aes(x = x,
                     y = y,
                     size = Deaths),
                 color = 'red',
                 alpha =0.001) +
      scale_size_area(name = '', breaks = c(100, 1000, 10000, 100000),
                      max_size = 25
                      ) +
    # scale_size_area(name = '', limits = c(1, 10), breaks = c(0, 10, 30, 50, 70, 100, 200, 500)) +
      labs(title = this_date) +
      theme(plot.title = element_text(size = 30),
            legend.text = element_text(size = 15),
            legend.position = 'left')
  
    plot_number <- add_zero(i, 3)
    ggsave(filename = paste0('animation/', plot_number, '.png'),
           plot = g,
           width = 9.5,
           height = 5.1)
  }
  setwd('animation')
  system('convert -delay 30x100 -loop 0 *.png result.gif')
  setwd('..')

}

Maps of Spain

make_map <- function(var = 'deaths',
                     data = NULL,
                     pop = FALSE,
                     pop_factor = 100000,
                     points = FALSE,
                     line_color = 'white',
                     add_names = T,
                     add_values = T,
                     text_size = 2.7){
  
  if(is.null(data)){
    data <- esp_df %>%  mutate(ccaa = cat_transform(ccaa))

  }

  left <- espf %>%   mutate(ccaa = cat_transform(ccaa)) 
  right <- data[,c('ccaa', paste0(var, '_non_cum'))]
  

  names(right)[ncol(right)] <- 'var'
  right <- right %>% group_by(ccaa) %>% summarise(var = sum(var, na.rm = T))
  
  if(pop){
    right <- left_join(right, esp_pop)
    right$var <- right$var / right$pop * pop_factor
  }
  map <- left_join(left, right)
  
  if(points){
    the_points <- centroids %>%
      left_join(right)
    g <- ggplot() +
      geom_polygon(data = map,
         aes(x = long,
             y = lat,
             group = group),
         fill = 'black',
         color = line_color,
         lwd = 0.4, alpha = 0.8) +
      geom_point(data = the_points,
                 aes(x = long,
                     y = lat,
                     size = var),
                 color = 'red',
                 alpha = 0.7) +
      scale_size_area(name = '', max_size = 20)
  } else {
    # cols <- c('#008080','#70a494','#b4c8a8','#f6edbd','#edbb8a','#de8a5a','#ca562c')
    cols <- RColorBrewer::brewer.pal(n = 8, name = 'Blues')
    g <- ggplot(data = map,
         aes(x = long,
             y = lat,
             group = group)) +
    geom_polygon(aes(fill = var),
                 lwd = 0.3,
                 color = line_color) +
      scale_fill_gradientn(name = '',
                           colours = cols)
    # scale_fill_viridis(name = '' ,option = 'magma',
    #                    direction = -1) 
  }
  
  # Add names?
  if(add_names){
    centy <- centroids %>% left_join(right)
    if(add_values){
      centy$label <- paste0(centy$ccaa, '\n(', round(centy$var, digits = 2), ')')
    } else {
      centy$label <- centy$ccaa
    }

    g <- g +
      geom_text(data = centy,
                aes(x = long,
                    y = lat,
                    label = label,
                    group = ccaa),
                alpha = 0.7,
                size = text_size)
  }
  
  g +
    theme_map() +
    labs(subtitle = paste0('Data as of ', max(data$date))) +
    theme(legend.position = 'right')
  
}

make_dot_map <- function(var = 'deaths',
                     date = NULL,
                     pop = FALSE,
                     pop_factor = 100,
                     point_factor = 1,
                     points = FALSE,
                     point_color = 'darkred',
                     point_size = 0.6,
                     point_alpha = 0.5){
  
  
  if(is.null(date)){
    the_date <- max(esp_df$date)
  } else {
    the_date <- date
  }
    right <- esp_df[esp_df$date == the_date,c('ccaa', var)]
   names(right)[ncol(right)] <- 'var'
  if(pop){
    right <- left_join(right, esp_pop)
    right$var <- right$var / right$pop * pop_factor
  }
  map_data <- esp1@data %>%
    left_join(names_df) %>%
      left_join(right)
  map_data$var <- map_data$var / point_factor
  out_list <- list()
  for(i in 1:nrow(map_data)){
    sub_data <- map_data[i,]
    this_value = round(sub_data$var)

    if(this_value >= 1){
      this_ccaa = sub_data$ccaa
      # get some points
      sub_points <- random_points %>% filter(ccaa == this_ccaa)
      sampled_points <- sub_points %>% dplyr::sample_n(this_value)
      out_list[[i]] <- sampled_points
    }
  }
  the_points <- bind_rows(out_list)
  
  g <- ggplot() +
    geom_polygon(data = espf,
         aes(x = long,
             y = lat,
             group = group),
         fill = 'white',
         color = 'black',
         lwd = 0.4, alpha = 0.8) +
    geom_point(data = the_points,
               aes(x = long,
                   y = lat),
               color = point_color,
               size = point_size,
               alpha = point_alpha)
  g +
    theme_map() +
    labs(subtitle = paste0('Data as of ', max(esp_df$date)))
  
}

Deaths

Absolute number of deaths: points

make_map(var = 'deaths',
       points = T) +
  labs(title = 'Number of deaths',
       caption = '@joethebrew')

Absolute number of deaths: choropleth

make_map(var = 'deaths',
         line_color = 'darkgrey',
       points = F) +
  labs(title = 'Number of deaths',
       caption = '@joethebrew')

Number of deaths adjusted by population: points

make_map(var = 'deaths', pop = TRUE, points = T) +
  labs(title = 'Number of deaths per 100,000',
       caption = '@joethebrew')

Number of deaths adjusted by population: polygons

make_map(var = 'deaths', pop = TRUE, points = F, line_color = 'darkgrey') +
  labs(title = 'Number of deaths per 100,000',
       caption = '@joethebrew')

Number of deaths: 1 dot per death

make_dot_map(var = 'deaths', point_size = 0.05) +
  labs(title = 'COVID-19 deaths: 1 point = 1 death\nImportant: points are random within each CCAA; do not reflect exact location',
       caption = '@joethebrew')

Cases

Absolute number of cases: points

make_map(var = 'cases',
       points = T) +
  labs(title = 'Number of confirmed cases',
       caption = '@joethebrew')

Absolute number of cases: choropleth

make_map(var = 'cases',
         line_color = 'darkgrey',
       points = F) +
  labs(title = 'Number of confirmed cases',
       caption = '@joethebrew')

Number of cases adjusted by population: points

make_map(var = 'cases', pop = TRUE, points = T) +
  labs(title = 'Number of confirmed cases per 100,000',
       caption = '@joethebrew')

Number of cases adjusted by population: polygons

make_map(var = 'cases', pop = TRUE, points = F,
         line_color = 'darkgrey') +
  labs(title = 'Number of confirmed cases per 100,000',
       caption = '@joethebrew')

Number of cases: points

make_dot_map(var = 'cases',
             point_size = 0.05, point_alpha = 0.5, point_factor = 10) +
  labs(title = 'COVID-19 cases: 1 point = 10 cases\nImportant: points are random within each CCAA; do not reflect exact location',
       caption = '@joethebrew')